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Contenuto fornito da Daliana Liu. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Daliana Liu o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
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The Data Scientist Show
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Contenuto fornito da Daliana Liu. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Daliana Liu o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
A deep dive into data scientists' day-to-day work, tools and models they use, how they tackle problems, and their career journeys. This podcast helps you grow a successful career in data science. Listening to an episode is like having lunch with an experienced mentor. Guests are data science practitioners from various industries, AI researchers, economists, and CTOs of AI companies. Host: Daliana Liu, an ex-Amazon senior data scientist with 180k followers on Linkedin. Join 20k subscribers at www.dalianaliu.com to learn more about data science, career, and this show. Twitter @DalianaLiu.
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90 episodi
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Manage series 3012777
Contenuto fornito da Daliana Liu. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Daliana Liu o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
A deep dive into data scientists' day-to-day work, tools and models they use, how they tackle problems, and their career journeys. This podcast helps you grow a successful career in data science. Listening to an episode is like having lunch with an experienced mentor. Guests are data science practitioners from various industries, AI researchers, economists, and CTOs of AI companies. Host: Daliana Liu, an ex-Amazon senior data scientist with 180k followers on Linkedin. Join 20k subscribers at www.dalianaliu.com to learn more about data science, career, and this show. Twitter @DalianaLiu.
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The Data Scientist Show
1 Why data scientists are tired, six real data scientists' frustrations - The Data Scientist Show #089 42:22
Daliana interviewed 6 data scientists from her meetup in New York City. It's a unique episode where you get to hear the real frustrations of data scientists. We talked about struggles working in healthcare, finance, data quality and AI, how to advocate for yourself, and align with your managers. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/…
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The Data Scientist Show
1 Why 80% of A/B tests fail, how to 10X your experimentation velocity - Kristi Angel - The Data Scientist Show #088 43:46
Most experimentations fail, Kristi Angel shares her expertise on scaling experimentation and avoiding common A/B testing pitfalls. Learn five things that can help boost test velocity, designing impactful experiments, and leveraging knowledge repos. (Chapters below) Kristi Angel’s LinkedIn: https://www.linkedin.com/in/kristiangel/ Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ (00:00:00) Intro (00:01:26) Why do most experimentations fail? (00:07:05) Mistakes in choosing metrics (00:10:05) Is revenue a good metric? (00:13:18) Split metrics in three ways (00:15:10) Daliana's story with too many category breakdowns (00:16:59) What makes the best data science team? (00:19:24) Data scientist work in silo vs in a data science team (00:21:15) Building a knowledge center (00:23:40) Example of knowledge center; nuance of experimentations (00:26:09) How many metrics and variants? (00:30:56) How to reduce noise - CUPED (00:33:01) Future of A/B testing (00:38:33) Q&A: Low statistical power…
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The Data Scientist Show
1 From physics PhD to data science leader, unexpected challenges in survey data, Python vs R, EDA best practices, building MLOps toolkit - Julia Silge - The Data Scientist Show #087 46:18
Julia Silge is an engineering manager at Posit PBC, formerly know as R-studio, where she leads a team of developers building open source software MLOps. Before Posit, she finished a PhD in astrophysics, worked for several years in the nonprofit space, and was a data scientist at Stack Overflow where some of her most public work involved the annual developer survey. We talked about MLOps tools, challenges in survey data, text analysis, and balancing her interests in data science and engineering. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ (00:00:00) Introduction (00:00:56) Getting into data science (00:04:50) Transition from data centers to engineering manager (00:14:04) Common challenges in tool development (00:17:38) Challenges with survey data (00:26:47) Engineering skills for data scientists (00:28:59) Balancing roles (00:34:49) Developing skills in Exploratory Data Analysis (EDA) (00:39:19) Python vs. R for data analysis (00:44:40) Exciting aspects in career and personal life…
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The Data Scientist Show
1 Why he created Pandas, the future of data systems, why he left his CTO role to become a chief architect - Wes McKinney - The Data Scientist Show #086 52:24
Wes McKinney is the co-creator of pandas library and he is the cofounder of Voltron data. Currently he is a principal Architect at Posit and an investor in data systems. Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ Wes' LinkedIn: https://www.linkedin.com/in/wesmckinn/ (00:00:00) Introduction (00:00:44) How Pandas Started (00:06:40) Voltron Data (00:10:03) Benefits of Easy-to-Use Data Tools (00:13:20) The Rise of New Data Tools (00:18:07) Choosing Tools: Vertical or Flexible? (00:23:01) Big Models and Data Tools (00:29:29) Challenges in Building a Product (00:31:28) Becoming a Top Architect (00:34:55) Missed Aspects of Previous Roles (00:39:04) A Busy Week: Advising, Designing, Investing (00:43:42) Improving Open Source (00:45:24) How to Decide What to Work On (00:46:28) What he’s learning now (00:47:56) Excitement in Career and Life (00:48:29) Using ChatGPT for Learning (00:50:27) Future Impact Goals…
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The Data Scientist Show
1 From financial analyst to director of analytics, how to get promoted quickly, 7 elements of influence - Christopher Fricker - The Data Scientist Show #085 1:13:51
1:13:51
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1:13:51Christopher Fricker is a senior director in analytics and BI at Renaissance Learning. He started his career in finance and later became a data science consultant working with Meta, Netflix, and pre-IPO tech companies doing analytics. We talked about the mental models that helped him grow from a finance analyst to an analytics leader. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Chris’ LinkedIn: https://www.linkedin.com/in/christopherfricker/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ (00:00:00) Introduction (00:01:46) How to get promoted quickly (00:08:40) Power vs authority (00:11:21) First principal thinking (00:32:34) ROI of a data team (00:40:29) How to be persuasive (00:54:52) All Data is wrong (00:56:22) How he audits the data (01:00:52) How to make someone help you at work…
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The Data Scientist Show
I interviewed Geoffery Angus, ML team lead @Predibase to talk about why adapter-based training is a game changer. We started with an overview of fine-tuning and then discussed five reasons why adapters are the future of LLMs. Later we also shared a demo and answered questions from the live audience. Try fine-tuning for free: https://pbase.ai/GetStarted Geoffrey’s LinkedIn:https://www.linkedin.com/in/geoffreyangus Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ Geoffrey’s LinkedIn: https://www.linkedin.com/in/geoffreyangus Try finetuning for free: https://pbase.ai/GetStarted (00:00:00) Intro (00:01:19) What is Fine-tuning? (00:08:18) Utilizing Adapters for Finetuning Enhancement (00:09:50) 5 reasons why adapters are the future of LLMs (00:26:34) Common Mistakes in Adapters Usage (00:28:34) Training Your Own Adapter (00:32:23) Behind the Scenes of the Adapter Training Process (00:37:51) Config File Guidance for Fine-Tuning (00:39:41) Debugging Strategies for Suboptimal Fine-Tuning Results (00:42:23) User Queries: Creating a LoRa Adapter and Future Support (00:51:06) Key Takeaways and Recap…
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The Data Scientist Show
1 Landing a job by analyzing Seattle's crime data, from data scientist to founder of interview query, building a lifestyle business - Jay Feng - The Data Scientist Show #083 35:41
Jay Feng created a viral project using Seattle crime data and later got into data science. He later founded "Interview Query" helping data scientists get jobs. We'll talk about how he landed his data science job through his blog, and his journey from data scientist to founder. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ Jay Feng's LinkedIn: https://www.linkedin.com/in/jay-feng-ab66b049/ Jay Feng's YouTube: https://www.youtube.com/c/DataScienceJay (00:00:00) Introduction (00:01:11) From engineer to data scientist (00:03:10) Got a job through a project (00:05:35) Daliana's portfolio project with Zillow (00:09:13) From data scientist to entreprenuer (00:13:19) "Tinder" for job (00:15:01) How he chose companies to work for (00:15:56) Why he became an entreprenuer (00:17:37) How many hours does he work (00:18:54) Challenges when building "interview query" (00:20:18) Speed vs scale (00:22:11) Growth hacks he used (00:24:22) YouTube vs newsletter (00:27:21) Lessons he learned as a CEO (00:29:16) How to grow from tech employee to founder (00:31:59) How he defines success (00:34:38) If you have a business idea for Jay…
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The Data Scientist Show
1 Case studies from the GenAI frontier, scaling ML teams, from biologist to machine learning consultant- Erik Gafni - The Data Scientist Show #082 1:03:40
1:03:40
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1:03:40Erik Gafni builds AI systems and teams. He founded Eventum AI (https://bit.ly/eventum-ai), an ML consulting company working with high-growth startups. We talked about GenAI projects he worked on, how he built production ML systems, how to scale ML teams, and his journey from biologist to ML researcher. Interested in working with Erik: https://bit.ly/erik-consulting Erik's LinkedIn: https://bit.ly/erik-gafni-LI (00:00:00) Introduction (00:01:59) Is GenAI overhyped? (00:04:28) Ascent translation with AI (00:11:58) Social media app with AI (00:14:00) Stable diffusion model evaluation (00:15:57) "Consult-to-hire" model (00:17:35) AI in biotech (00:22:46) Self-supervised learning (00:31:22) How he hires people (00:33:19) Research vs production (00:35:57) Is AGI coming? (00:37:30) New trends in GenAI (00:41:45) Data quality in GenAI (00:42:58) Philosophy in LLMs (00:49:48) OpenAI vs Open Source (00:53:58) Mistakes he made (00:57:41) How did he get into ML…
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The Data Scientist Show
1 Data science job market in 2024, softskills for interviews, AI engineering - Jay Feng - The Data Scientist Show #081 1:07:14
1:07:14
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1:07:14Jay Feng is the CEO of interview query, a service that help data scientists get jobs. Previously he worked as a data scientist at Nextdoor, Monster. We talked about data science job market, the rise of AI engineering, and the softskills people overlook during interviews. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ Jay Feng's LinkedIn: https://www.linkedin.com/in/jay-feng-ab66b049/ Jay Feng's YouTube: https://www.youtube.com/c/DataScienceJay 00:00:00 Introduction 00:01:11 Data science job market in 2024 00:09:13 Build projects with AI 00:16:19 Softskills in interviews 00:23:18 Daliana's story on "socializing ideas" 00:28:38 Common mistakes in interviews 00:35:30 Product DS vs ML interviews 00:36:27 Product analytics interview questions 00:39:18 Career transition in DS 00:43:04 Jay's career journey 00:45:38 Is there a principal data analyst? 00:51:52 AI engineer 00:54:28 New roles vs obsolete roles in DS 01:04:46 Is data science dead?…
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The Data Scientist Show
1 How to handle being laid off (as data scientists), severance negotiation, full-time employment vs independent consultant - The Data Scientist Show #080 1:06:33
1:06:33
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1:06:33We are joined by two data scientists who have firsthand experience with layoffs. We’ll talk about how to negotiate severance packages, how to handle stress, strategies for job hunting post-layoff, and how to reduce risks in full-time employment. Working with Daliana on personal branding: https://forms.gle/heNuZzaHjaAMQwLu6 Her email: daliana@dalianaliu.com Guests: Susan Shu Chang: Linkedin: https://www.linkedin.com/in/susan-shu-chang/ Newsletter: susanshu.substack.com Sundar Swaminathan Linkedin: https://www.linkedin.com/in/sswamina3/ Website: https://www.sundarswaminathan.com/ ( 00:00:00 ) Introduction ( 00:06:13 ) Severance Negotiation ( 00:20:29 ) Identity crisis ( 00:26:22 ) Job search after layoff ( 00:30:21 ) Networking ( 00:35:23 ) Risk at pre-seed startups ( 00:37:03 ) How should data scientists pick companies ( 00:40:43 ) What to ask hiring managers ( 00:45:01 ) Does GenAI change interview processes? ( 00:47:17 ) Are data science teams getting leaner? ( 00:48:56 ) Future of data science roles ( 00:50:37 ) Full time employment and job security ( 00:53:46 ) Benefits of full time jobs ( 00:58:14 ) Reduce risk of being laid off ( 01:00:43 ) How to sell yourself ( 01:02:43 ) How to plan your finances ( 01:05:09 ) How to become an independent consultant…
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The Data Scientist Show
1 From data analyst to sales engineer, personality-based career design, sales skills for data people - Jenny Wu - The Data Scientist Show #079 57:26
Jenny Wu is a data analyst turned sales engineer for data products at Hex. We talked about sales engineer vs data analyst, how to design a career based on your personality, and how to transition into a customer-facing role. Jenny’s LinkedIn: https://www.linkedin.com/in/jenny-wu-... Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ (00:00:00) Introduction (00:01:34) What is a Sales Engineer? (00:09:35) Sales Engineering Day-to-Day (00:13:09) Challenge in sales (00:21:37) Traits of Successful Salespeople (00:30:32) Stakeholder Engagement (00:36:24) Getting into customer-facing roles (00:43:55) Quitting her job to travel the world (00:48:05) Advice on Career Breaks (00:50:39) Embedding Career and Personal Goals (00:51:57) How do you achieve happiness?…
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The Data Scientist Show
1 The future of data science teams, integrating AI into data science workflows, building data apps for stakeholders - Barry McCardel - The Data Scientist Show #078 1:04:56
1:04:56
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1:04:56Barry McCardel is the cofounder and CEO of Hex(free trial: hex.tech/dsshow), a collaborative data workspace. Their customers include FiveTran, Notion, and Anthropic. We talked about what does the future of data team look like, how to tackle challenges of data team collaborations, and how to leverage AI in data science’s workflow. 60-day Free Trial: hex.tech/dsshow Barry’s LinkedIn: https://www.linkedin.com/in/barrymccardel (00:00:00) Introduction (00:01:25) Is AI replacing data scientists? (00:06:08) Are data science teams getting smaller? (00:09:54) What is Hex? (00:11:24) How to communicate with stakeholders (00:24:29) Should data scientists be full stack? (00:31:23) How data team measure ROI (00:33:35) Quantitative vs qualitative analysis (00:35:33) When you shouldn't use data? Data vs product intuition (00:41:39) How to hire your first data team? (00:48:59) Is the modern data stack dead? (00:53:55) GenAI in data science workflows (00:59:03) Future of data scientist (01:02:30) New features in Hex…
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The Data Scientist Show
1 Product data science for Microsoft AI, data scientist's role of GenAI, how to deal with burn out - Sid Sharan - The Data Scientist Show #077 58:57
Siddhartha Sharan is a Senior Data and Applied Scientist at Microsoft, helping product teams make data-driven decisions. Currently he is working on an AI product built with OpenAI APIs for sentiment analysis. We talked about how he evaluates AI products built with large language models at Microsoft, product data science, and how he went from a business background to data science. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Sid’s LinkedIn: https://www.linkedin.com/in/siddharthasharan/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ (00:00:00) Introduction (00:05:20) How does Microsoft evaluate AI product (00:16:17) Using OpenAI API for sentiment analysis (00:25:29) Microsoft data science team culture (00:26:52) DS, PM collaboration (00:28:29) Three steps to build trust in data science (00:30:13) How did he got into Microsoft (00:34:09) Level up in Genetech (00:36:09) ML engineer vs Product DS (00:37:43) Core skills in product DS (00:40:20) Hiring (00:42:47) How to deal with burnout (00:45:03) Should you over work to earn trust? (00:45:44) Daliana's story about first day at Amazon (00:49:54) Will AI replace data scientists? (00:51:32) Data scientist's role of GenAI (00:54:32) How to keep up with GenAI…
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The Data Scientist Show
1 How she doubled her salary in a year as a data analyst, SQL in the real world, is job hopping bad? - Jess Ramos - The Data Scientist Show #076 1:07:48
1:07:48
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1:07:48Jess Ramos is a Senior Data Analyst at Crunchbase, a LinkedIn Learning Instructor, and a content creator in the data space. She has a bachelor's degree in Math, Spanish, and Business from Berry University and a master's in Business Analytics from University of Georgia. Today we’ll talk about SQL in the real world, data analyst vs data scientist, is job hopping bad, how she negotiated her salary. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Jess’ Linkedin: https://www.linkedin.com/in/jessramosmsba/ Preparing to Get a Job in Data Analytics: shorturl.at/sCNPT Solve Real-World Data Problems with SQL: https://bit.ly/3Zq6wnd Big Data Energy Newsletter: https://bit.ly/46x4rIR Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ (00:00:00) Introduction (00:01:24) Why Jess left her job at Freddie Mac (00:03:25) Is job hopping bad (00:04:42) How to explain short job stints when interviewing (00:06:49) Jess's day-to-day work and tech stack (00:09:15) SQL in the real world (00:12:10) How to talk data to stakeholders (00:18:33) How Jess prepares for SQL interviews (00:28:11) Data analysts vs data scientists (00:32:11) Choosing a career path (00:47:19) How to ask recruiter questions (00:50:15) Jess's LinkedIn content creation journey (00:59:03) The future of Jess's career (01:03:42) Jess's favorite books…
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The Data Scientist Show
1 How he got into machine learning and Gen AI at Amazon, how we went from "enemies" to allies - Mehdi Noori - The Data Scientist Show #075 1:32:22
1:32:22
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1:32:22Mehdi Noori is an applied science manager at the Generative AI Innovation Center at Amazon. I used to work with Mehdi while we were at the Machine Learning Solutions Lab at AWS. So before Amazon, Mehdi was a data scientist working on marketing intelligence. Mehdi has a PhD from University of Central Florida in civil engineering and sustainability. Subscribe to Daliana's newsletter for more on data science and career www.dalianaliu.com Mehdi Noori: https://www.linkedin.com/in/mehdi-noori/ Predicting Soccer Goals: https://aws.amazon.com/blogs/machine-learning/predicting-soccer-goals-in-near-real-time-using-computer-vision/…
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The Data Scientist Show
1 Why she quit her finance job to become a farmer, exploring a different path from the modern life - Misty Arnold - The Data Scientist Show #074 1:10:28
1:10:28
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1:10:28My friend Misty moved to a farm in Portugal after her 20 years of career in finance. We talked about her experience moving from the busy corporate life to the farm life where she does a lot of manual work. Was it challenging, how does her finance work, and what is her advice to other people who also want to explore a different path outside of the modern city life. I hope this episode will give you a different perspective about your career. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ (00:00:00) Introduction (00:11:41) Life on the farm (00:15:46) Her finance plans (00:22:55) Her career journey (00:27:14) What do accountants do (00:32:29) I thought I would be happy (00:41:25) Daliana's personal view about finance; when it's enough for you (00:44:41) Does she feel lonely on a farm? (00:48:39) What if she didn't leave the corporate world? (00:54:07) Does she regret her decision…
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The Data Scientist Show
1 Why he left his MLE job for product data science at Meta, data science at Uber, Linkedin, and Truecar - Pan Wu - The Data Scientist Show #073 1:13:01
1:13:01
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1:13:01Pan Wu is a senior manager of data science at Meta. We talked about why he moved from machine learning to product data science, projects he worked on at Uber, Linkedin, and Meta, and how he transitioned from IC to manager. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Pan’s LinkedIn: https://www.linkedin.com/in/panwu/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ (00:00:00) Introduction (00:01:30) Why he transitioned from MLE to product DS (00:07:38) Meta data scientists skill sets (00:15:49) When did his interest shifted from MLE to product DS (00:18:04) Is MLE more respected? (00:25:46) A/B testing deep dives in 3 steps (00:28:21) Built a tool at Linkedin (00:35:52) How to sell your project (00:41:07) Junior vs senior data scientist (00:43:24) From staff data scientist to manager (00:45:18) Explore being a manager (00:46:24) Cultures in Uber, Linkedin, TrueCar (00:52:09) Data science over the past 10 year (00:55:06) MLE vs DS fun and frustration (00:57:26) Product DS reality (00:59:10) Learning new skills (01:01:39) Mistakes he made (01:06:34) Future of data science (01:08:04) Will data scientists be replaced by AI (01:09:42) Three skills he looks for when hiring…
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The Data Scientist Show
1 Machine learning in cybersecurity, computer vision in sports, from business analyst to ML engineer - Betty Zhang - The Data Scientist Show #072 55:12
Betty Zhang is a data scientist currently working at a cloud security company, previously she was a data scientist at Amazon Web Services. Today we’ll talk about her computer vision projects in Sports, data science use cases in cyber security, from business major to data scientist, what’s her experience working in startups vs big tech companies. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Betty’s Linkedin: https://www.linkedin.com/in/betty-zhang-0bb63731/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/ (00:00:00) Introduction (00:01:21) Computer Vision Project in Sports at AWS (00:12:28) Challenges in computer vision (00:14:02) Time allocation for ML projects (00:15:22) 3 key skills for computer vision (00:17:20) From business analyst to ML engineer (00:18:14) How she got her data scientist job through Linkedin (00:21:32) How she got into Amazon (00:22:17) Three tech skills needed during Amazon interviews (00:26:11) Why she joined a Cyber Security startup (00:27:22) Three cybersecurity use cases (00:29:47) Anomaly detection (00:30:40) ML for cybersecurity (00:34:43) Tech stacks Amazon vs Startups (00:39:35) Startups vs big tech (00:45:56) Balance learning and impact (00:48:35) Advice for new data scientists…
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The Data Scientist Show
1 Stop abusing A/B testing, toxic experimentation culture, how to run A/B tests with rigor - Che Sharma - The Data Scientist Show #071 1:03:42
1:03:42
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1:03:42Che Sharma came back to discuss toxic behaviors in experimentation culture and provide actionable advice on how to handle those situations, how to have rigor and integrity when designing and analyzing A/B tests. Che was the 4th data scientist at Airbnb, later he joined Webflow as an early employee. In 2021 he founded Eppo, a next-gen A/B experimentation platform designed for modern data and product teams to run more trustworthy and advanced experiments. We talked about A/B testing best practices, A/B testing for ML models, and Che’s career journey. Reach out to Che: https://www.linkedin.com/in/chetanvsharma/…
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1 Academia vs. Industry for Machine Learning, Research at Uber AI Labs, ML for Wind Farms - Jason Yosinski - The Data Scientist Show #070 1:16:09
1:16:09
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1:16:09Jason Yosinski was a founding member of Uber AI Labs. He is also a co-founder of WinscapeAI a company dedicated to using custom sensor networks and machine learning to increase the efficiency and sustainability of wind farms. Jason holds a PhD in computer science from Cornell University. We talked about his experience at Uber AI, his research in deep learning, and ML for wind farms. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Jason’s Website: https://yosinski.com/ Jason’s LinkedIn: https://www.linkedin.com/in/jasonyosinski/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu ( 00:00:00) Introduction ( 00:06:06 ) His advice for Uber ML teams ( 00:16:03 ) From research to industry ( 00:20:24 ) ML for wind farms ( 00:25:40 ) Metrics for wind energy prediction ( 00:29:23 ) Start with a small dataset ( 00:32:00 ) ML in academia vs. the industry ( 00:33:24 ) Do you need a PhD for ML? ( 00:38:14 ) Daliana's story about grad school ( 00:41:37 ) The value of a PhD ( 00:43:13 ) ML Collective ( 00:48:3 6 ) Technical communication ( 00:57:21 ) ML Skillsets ( 00:59:45 ) Future of machine learning ( 01:05:23 ) Personal development: Hoffman process ( 01:15:13 ) Do things that excites you…
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The Data Scientist Show
1 Ads forecasting at Netflix and Spotify, how to build your personal moat - Jeff Li - The Data Scientist Show #069 1:26:29
1:26:29
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1:26:29Jeff Li is a senior data scientist at Netflix, focusing on Ads forecast. Previously he was a data science manager at Spotify, worked on supply forecasting, demand forecasting, and data infrastructure. He studied business at the University of Southern California. We talked about Ads forecasting, career path as a manager vs IC, culture in Spotify vs Netflix vs Doordash. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Jeff Li’s LinkedIn: https://www.linkedin.com/in/lijeffrey/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu ( 00:00:00 ) Introduction ( 00:00:45 ) Got into data science from poker and consulting ( 00:07:54 ) Ads forecasting at Netflix and Spotify ( 00:13:30 ) From IC to manager to IC ( 00:14:53 ) how to measure forecasting models ( 00:21:58 ) collaborating with stakeholders in sales ( 00:29:44 ) how he became an expert in ads forecasting ( 00:49:57 ) impact sizing at Doordash ( 00:57:34 ) Company culture differences (DoorDash, Spotify, Netflix) ( 01:12:47 ) how he wants to grow his career…
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The Data Scientist Show
1 A/B testing at Airbnb, building next-gen experimentation platform at Eppo - Che Sharma - The Data Scientist Show #068 1:14:15
1:14:15
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1:14:15Che Sharma was the 4th data scientist at Airbnb, later he joined Webflow as an early employee. In 2021 he founded Eppo, a next-gen A/B experimentation platform designed for modern data and product teams to run more trustworthy and advanced experiments. We talked about A/B testing best practices, A/B testing for ML models, and Che’s career journey. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Che’s LinkedIn: https://www.linkedin.com/in/chetanvsharma/ Try Eppo for A/B testing: https://www.geteppo.com/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu (00:00:00) Introduction (00:01:26) Getting started in data science at Airbnb (00:03:08) Keys to successful A/B testing (00:06:53) Interpreting and communicating A/B test results (00:15:00) A/B testing best practices testing machine learning models (00:41:39) Centralizing experiment analysis (00:53:46) Preparing data scientists for the future (00:59:33) Developing communication skills as a data scientist (01:08:43) Transitioning from individual contributor to manager (01:12:28) The future of experimentation…
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The Data Scientist Show
1 From data scientist@Meta to full-time YouTuber (500k+ sub), AI engineering, future of work - Tina Huang - The Data Scientist Show #067 1:54:52
1:54:52
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1:54:52We talked about self-learning, productivity, how Tina navigates her career change and how she thinks AI could change the future of work. Tina's YouTube: www.youtube.com/@TinaHuang1 Lonely Octopus: www.lonelyoctopus.com Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Tina Huang is a data scientist turned YouTube creator with 500k subscribers. She is the founder of Lonely Octopus, an online program helping people gain data science, AI, and freelancing skills. She originally studied pharmacology before transitioning into tech, completing a master's degree in computer science at UPenn. (00:02:38) Transitioning from Data Science to Content Creation (00:06:29) Preparing for Data Science Interviews (00:10:59) Starting a YouTube Channel (00:14:18) Building Multiple Income Streams (00:17:35) Getting Started with AI Skills (00:29:29) Advice for Starting YouTube (00:34:47) Improving Storytelling Skills (00:36:58) Overcoming Procrastination (00:42:33) The Future of Work (01:47:08) Looking to the Future (01:26:49) Income Breakdown…
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The Data Scientist Show
1 Making LLMs hallucinate less, how to diagnose ML models, from PM in Google AI to CEO of Galileo - Vikram Chatterji - The Data Scientist Show #066 1:26:50
1:26:50
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1:26:50Vikram is the co-founder of Galileo – an AI diagnostics and explainability platform used by data science teams building NLP, LLMs and Computer Vision models across the Fortune 500 and high growth startups. Prior to Galileo, Vikram led Product Management at Google AI, where his team built models for the Fortune 2000 across retail, financial services, healthcare and contact centers. He has a master degree from Carnegie Mellon University from the school of computer science. If you enjoy the show, subscribe to the channel and leave a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Resources:LLM Studio: https://www.rungalileo.io/blog/announcing-llm-studio Galileo: https://www.rungalileo.io/ Blog on LLM Hallucination: https://thesequence.substack.com/p/guest-post-stop-hallucinations-from Vikram Chatterji’s LinkedIn: https://www.linkedin.com/in/vikram-chatterji/ "The Mom Test": https://www.amazon.com/The-Mom-Test-Rob-Fitzpatrick-audiobook/dp/B07RJZKZ7F Daliana's Twitter: https://twitter.com/DalianaLiu Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu (00:00:00) Introduction (00:04:24) How he got into machine learning (00:06:53) Diagnosing large language models (00:09:56) Addressing model hallucination (00:12:46) Metrics for measuring hallucination (00:17:30) From Google AI to starting Galileo (00:24:08) Developing LLMs and putting them into production (00:32:51) Galileo's diagnostics and explainability platform (00:43:16) Advice for data scientists when joining a startup…
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The Data Scientist Show
1 Data Science "Mix Martial Arts", applied re-inforcement learning, scaling AI workloads using Ray - Max Pumperla - The Data Scientist Show #065 1:53:28
1:53:28
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1:53:28Max Pumperla designed his own career path in data science. He is a freelance software engineer at AnyScale, and also a data science professor. We talked about reinforcement learning, open source contributions, Ray for data scientists, and his view on the data scientists role. If you enjoy the show, subscribe to the channel and leave a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Max’s LinkedIn: https://www.linkedin.com/in/max-pumperla-a8099354/ Max's GitHub: https://github.com/maxpumperla Daliana's Twitter: https://twitter.com/DalianaLiu Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu (00:00:00) Introduction (00:09:19) How he got a remote job through Twitter (00:14:06) Introduction to Ray (00:18:52) Reinforcement learning (00:23:56) Key lessons on integrating customer feedback (00:35:12) Flaws in data science job titles (00:45:51) How to be irreplaceable as a data scientist (00:48:55) An unconventional career path as a data scientist (01:12:24) Productivity and work-life balance (01:28:10) Advice for building a personal brand…
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The Data Scientist Show
1 Uber's ML Systems (Uber Eats, Customer Support), Declarative Machine Learning - Piero Molino - The Data Scientist Show #064 1:50:05
1:50:05
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1:50:05Piero Molino was one of the founding members of Uber AI Labs. He worked on several deployed ML systems, including an NLP model for Customer Support, and the Uber Eats Recommender System. He is the author of Ludwig , an open source declarative deep learning framework. In 2021 he co-founded Predibase , the low-code declarative machine learning platform built on top of Ludwig. Piero's LinkedIn: https://www.linkedin.com/in/pieromolino Predibase free access: bit.ly/3PCeqqw Daliana's Twitter: https://twitter.com/DalianaLiu Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu (00:00:00) Introduction (00:01:54) Journey to machine learning (00:03:51) Recommending system at Uber Eats (00:04:13) Projects at Uber AI (00:09:34) Uber's customer obsession ticket system (00:16:01) How to evaluate online-offline business and model performance metrics (00:17:16) Customer Satisfaction (00:28:38) When do you know whether a project is good enough (00:41:50) Declarative machine learning and Ludwig (00:45:32) Ludwig vs AutoML (00:54:44) Working with Professor Chris Re (00:58:32) Why he started Predibase (01:07:56) LLM and GenAI (01:10:17) Challenges for LLMs (01:22:36) Advice for data scientists (01:34:29) Career advice to his younger self…
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The Data Scientist Show
1 Data science in transportation, the intersection of operations research and ML - Holger Teichgraeber - The Data Scientist Show #063 46:53
Holger Teichgraeber is a Data Science Manager at Archer Aviation. Previously, he worked at Convoy as a Research Scientist on their trucking marketplace, and at various companies in the energy space. Holger has a Bachelor's degree in Mechanical Engineering from Aachen, Germany, and a Masters and Ph.D. with research focus on machine learning and optimization applied to energy systems from Stanford University. He regularly writes on LinkedIn, with the goal to show how to build valuable products at the intersection of machine learning and optimization in production. If you enjoy the show, subscribe to the channel and leave a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career. Holger's LinkedIn: https://www.linkedin.com/in/holgerteichgraeber/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu (00:00:00) Introduction (00:01:28) How he got into operations research (00:02:39) Operation research vs data science (00:04:37) Trucking optimization at Convoy (00:08:42) Optimization problem (00:10:18) Strategic planning on air mobility at Archer (00:13:50) Using simulation and solving a problem (00:16:45) Big data science work vs smaller data science work (00:21:23) Stakeholder management (00:29:28) IC vs Manager (00:32:04) Advice on promotion (00:39:12) Work cultures in Germany and the US (00:41:16) How to handle tight deadlines (00:43:21) Important feedback from his work (00:44:14) How to plan projects (00:44:45) Next big challenge for data science teams (00:45:40) Career growth in the next few years (00:46:01) Connect with Holger…
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The Data Scientist Show
1 Tackling data quality issues, 5 pillars of data observability, from management consultant to CEO of Monte Carlo - Barr Moses -The Data Scientist Show #062 1:21:31
1:21:31
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1:21:31Barr Moses is a consultant turned CEO & Co-Founder of Monte Carlo, a data reliability company. She started her career as a management consultant at Bain & Company and a research assistant at the Statistics Department at Stanford University. Later, she became VP of Customer Operations at customer success company Gainsight, where she built the data and analytics team. She also served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford with a B.Sc. in Mathematical and Computational Science. Today, we’ll talk about Barr’s career journey, data reliability and observability, and what it means for data teams. If you enjoy the show, subscribe to the channel and leave a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science. Barr's LinkedIn: https://www.linkedin.com/in/barrmoses/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu (00:00:00) Introduction (00:01:24) How did she got into data science (00:08:26) Frameworks for data-driven decisions (00:11:20) Is customer support ticket always bad? (00:15:20) How to quickly find out what is true (00:20:17) Struggles in the data team (00:23:37) Daliana’s story about lineage (00:28:00) People stressed about data (00:28:09) Netflix was down because of wrong data (00:30:40) Common issues with data quality (00:33:14) 5 pillars of data observability (00:39:14) How does Monte Carlo help data scientists (00:43:08) Build in-house vs adopt tools (00:45:48) How Daliana fixed a data quality issue (01:02:44) How to measure the impact of the data team (01:09:09) Mistakes she made (01:15:28) Beat the odds…
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The Data Scientist Show
1 Is search dead? Google vs ChatGPT, from Google Search to enterprise search at Glean, machine learning in search, tech layoffs - Deedy Das - The Data Scientist Show #061 1:27:06
1:27:06
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1:27:06Deedy Das is a founding engineer at Glean, an enterprise search startup. Previously, he was a Tech Lead at Google Search working on query understanding and the sports product in New York, Tel Aviv, and Bangalore. Before that, he was an engineer at Facebook New York and graduated from Cornell University. Outside of work, Deedy writes on his blog. He published a viral resume template and his work on exposing grading flaws in the Indian education system. He also enjoys running marathons, road cycling, and playing cricket. Today we’ll talk about the search projects he worked on at Google, why he left Google, his current work at Glean, and his thoughts on whether Google is doomed because of ChatGPT. If you enjoy the show, subscribe to the channel and leave a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science. Deedy's Twitter: https://twitter.com/debarghya_das?s=20 Daliana's Twitter: https://twitter.com/DalianaLiu Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu (00:00:00) Introduction (00:01:52) What is search (00:04:33) Query understanding (00:12:46) Google vs ChatGPT (00:18:24) Fixing bug for Sundar Pichai (00:27:33) Why he left google (00:30:32) How to get into search (00:34:38) Enterprise search at Glean (00:46:55) Advice for people who got laid off (00:48:41) What do search engineers do (00:51:37) How he evaluates candidates (00:53:58) Future of search (00:57:16) Why the web is declining (00:59:25) Copilot and AI-powered developer tools (01:03:46) Indian startup ecosystem (01:07:45) India vs Silicon Valley (01:09:48) How he grew 30k followers on Twitter (01:13:28) Daliana and Deedy’s challenge with social media (01:19:31) Career mistakes he made…
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The Data Scientist Show
1 The 100-hour work week of an self-taught machine learning researcher, how he got into Google Brain, why he started Omni - Jeremy Nixon - The Data Scientist Show #060 1:42:52
1:42:52
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1:42:52Jeremy Nixon is a machine learning researcher, software engineer, and startup founder. Previously he was a software engineer at Google Brain working on deep learning. Now, he is the co-founder and CEO of Omni, building an immersive information retrieval system for you and your team. He studied applied math at Harvard University. Today we’ll talk about how he got into Google brain, his 3-month self-learning plan to learn machine learning, his startup, and how he executed his goal relentlessly since 2016. If you enjoy the show, subscribe to the channel and leave a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science. Jeremy's Twitter: https://twitter.com/JvNixon Jeremy's Blog: https://jeremynixon.github.io/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu Jeremy's LinkedIn: https://www.linkedin.com/in/jeremyvnixon (00:00:00) Introduction (00:01:50) Research in Google Brain (00:03:37) How he got into Google Brain (00:07:56) His 3-month plan to learn ML (00:17:55) The 100-hour workweek (00:33:26) What if he is tired (00:39:59) Why he found Omni (00:44:24) Data science problems in Omni (00:54:42) Future of machine learning (00:57:51) Silicon Valley is very accessible (00:59:47) The golden handcuffs (01:06:58) From data scientist to full-stack engineer (01:09:06) Close-minded data scientists (01:24:10) Advice to ML learners (01:29:41) Something he wished that he did when he was younger (01:37:25) The future of his career (01:42:17) Connect with Jeremy…
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