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Making Data Science Bilingual - Najat Khan

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Contenuto fornito da DataRobot. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da DataRobot 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.

Featuring:

Dr Najat Khan, Chief Data Science Officer, Janssen Global sits down with Ari Kaplan, AI Evangelist, DataRobot, for this episode of the More Intelligent Tomorrow podcast.

Najat Khan thrives in her mission to solve the world's most challenging health problems. With data science, AI, and machine learning, she endeavors to bring critical medicines to the public with efficiency, effectiveness, and inclusivity.

When COVID-19 hit, working for Janssen Global and with Johnson and Johnson, Khan set about constructing a team of more than 100 “bilingual” data scientists. Not only did they understand the language of data science, they also brought medical expertise like a PhD in neuroscience or a background in oncology.

“Every single thing we do needs to be purpose driven. You can’t start with ‘I saw this really cool algorithm that was published. We should do something with it’. If you go after the shiny objects, it doesn't work. You have to continually ask, ‘What’s the business problem we’re trying to solve?’ To me that is the critical foundation.”

With the business problem fully articulated, she embedded her team into every product, regulatory, clinical, and operational group tackling COVID-19. They worked “shoulder to shoulder” through every critical decision point along the vaccine building workflow.

To accelerate the process of identifying global hotspots and potential risks for vaccine trials, they partnered with MIT to build what would become one of the world’s largest machine learning programs. The resulting longitudinal dataset included socioeconomic, racial, and health data, with higher-than-census diversity and inclusiveness factors.

“The hardest part was not just the technical aspect, but also the cultural change management. When a company has never used one of the biggest machine learning programs, and all of humanity is watching to see if it is successful, it takes a lot of guts, will, and fortitude to see it all the way through to measurable impact.”

Listen to this episode of More Intelligent Tomorrow, to learn:

  • How data science can help us to be better prepared for the next global health crisis
  • Where the life sciences research and industry is headed in the next 5, 10, and 50 years
  • How data science, AI, and machine learning can change health care

“We can never say what we're doing in data science today is enough. We need to constantly push boundaries.”

  continue reading

69 episodi

Artwork
iconCondividi
 

Fetch error

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Manage episode 320445781 series 2842356
Contenuto fornito da DataRobot. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da DataRobot 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.

Featuring:

Dr Najat Khan, Chief Data Science Officer, Janssen Global sits down with Ari Kaplan, AI Evangelist, DataRobot, for this episode of the More Intelligent Tomorrow podcast.

Najat Khan thrives in her mission to solve the world's most challenging health problems. With data science, AI, and machine learning, she endeavors to bring critical medicines to the public with efficiency, effectiveness, and inclusivity.

When COVID-19 hit, working for Janssen Global and with Johnson and Johnson, Khan set about constructing a team of more than 100 “bilingual” data scientists. Not only did they understand the language of data science, they also brought medical expertise like a PhD in neuroscience or a background in oncology.

“Every single thing we do needs to be purpose driven. You can’t start with ‘I saw this really cool algorithm that was published. We should do something with it’. If you go after the shiny objects, it doesn't work. You have to continually ask, ‘What’s the business problem we’re trying to solve?’ To me that is the critical foundation.”

With the business problem fully articulated, she embedded her team into every product, regulatory, clinical, and operational group tackling COVID-19. They worked “shoulder to shoulder” through every critical decision point along the vaccine building workflow.

To accelerate the process of identifying global hotspots and potential risks for vaccine trials, they partnered with MIT to build what would become one of the world’s largest machine learning programs. The resulting longitudinal dataset included socioeconomic, racial, and health data, with higher-than-census diversity and inclusiveness factors.

“The hardest part was not just the technical aspect, but also the cultural change management. When a company has never used one of the biggest machine learning programs, and all of humanity is watching to see if it is successful, it takes a lot of guts, will, and fortitude to see it all the way through to measurable impact.”

Listen to this episode of More Intelligent Tomorrow, to learn:

  • How data science can help us to be better prepared for the next global health crisis
  • Where the life sciences research and industry is headed in the next 5, 10, and 50 years
  • How data science, AI, and machine learning can change health care

“We can never say what we're doing in data science today is enough. We need to constantly push boundaries.”

  continue reading

69 episodi

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