REAL WORLD EVIDENCE, SENIOR SCIENTIST

Boston, Massachusetts
US$125000 - US$140000 per annum + BENEFITS

REAL WORLD EVIDENCE, SENIOR SCIENTIST

BOSTON, MA

$125,000 - $140,000 + BENEFITS

THE COMPANY

This pharmaceutical startup is advancing drug discovery through artificial intelligence, machine learning, and deep learning. As a senior scientist on the clinical team, you will utilize your experience in epidemiology, electronic health records (EHR), electronic medical records (EMR), and real world evidence (RWE) to develop comprehensive data for commercial use.

THE ROLE

Responsibilities will include:

  • Working with active clinical programs
  • Developing new methods for upcoming clinical programs
  • Cross functionally collaborating with wet lab and dry lab scientists

YOUR SKILLS AND EXPERIENCE

Your skills include:

  • PhD in Epidemiology or related fields
  • MS in Epidemiology with industry experience
  • Demonstrated experience in analyzing epidemiological data, EMR/EHR data, and real world evidence
  • Demonstrated experience in R, Python, and SQL
  • Start-up experience is a plus

THE BENEFITS

  • Base salary range of $125,000 - $140,000
  • Comprehensive healthcare, vision, and dental
  • Huge internal growth opportunities, ability to drive clinical direction, and more!

HOW TO APPLY

Please email your CV to Alyssa Liew at the address .

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AL/65963
Boston, Massachusetts
US$125000 - US$140000 per annum + BENEFITS
  1. Permanent
  2. Health Informatics

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Why You Should Always Be Learning In Data Science: Tips From Kevin Tran

Last month we sat down with Kevin Tran, a Senior Data Scientist at Stanford University, to chat about Data Science trends, improvements in the industry, and his top tips for success in the market.  As one of LinkedIn’s Top Voices of 2019 within Data & Analytics. his thoughts on the industry regularly garner hundreds of responses, with debates and discussions bubbling up in the comments from colleagues eager to offer their input.  This online reputation has allowed him to make a name for himself, building out his own little corner of the internet with his expertise. But for Tran, it’s never been about popularity. “It’s not about the numbers,” he says without hesitation. “I don’t care about posting things just to see the number of likes go up.” His goal is always connection, to speak with others and learn from them while teaching from his own background. He’s got plenty of stories from his own experiences. For him, sharing is a powerful way to lead others down a path he himself is still discovering.  When asked about the most important lesson he’s learned in the industry, he says it all boils down to staying open to new ideas.  “You have to continue to learn, and you have to learn how to learn. If you stop learning, you’ll become obsolete pretty soon, particularly in Data Science. These technologies are evolving every day. Syntax changes, model frameworks change, and you have to constantly keep yourself updated.”  He believes that one of the best ways to do that is through open discussion. His process is to share in order to help others. When he has a realisation, he wants to set it in front of others to pass along what he’s learned; he wants to see how others react to the same problem, if they agree or see a different angle. It’s vital to consider what you needed to know at that stage. Additionally, this exchange of ideas allows Tran to learn from how others tackle the same problems, as well as get a glimpse into other challenges he may have not yet encountered.  “When I mentor people, I’m still learning, myself,” Tran confesses. “There’s so much out there to learn, you can’t know it all. Data Science is so broad." At the end of the day, it all comes down to helping each other and bringing humanity back to the forefront. In fact, this was his biggest advice for both how to improve the industry and how to succeed in it. It’s a point he comes back to with some regularity in his writing. “It doesn’t matter how smart you are, stay humble and respect everyone,” one post reads. “Everyone can teach you something you don’t know.” Treating people well, understanding their needs, and consciously working to see them as people instead of numbers or titles—this, Tran argues, is how you succeed in the business. To learn and grow, you must work with people, especially people with different skills and mindsets. Navigating your career is not all technical, even in the world of Data. “The thing that cannot be automated is having a heart,” he tells me sagely. Beyond this, Tran stresses the need for a solid foundation. The one thing you can’t afford to do is take shortcuts. You have to learn the practicalities and how to apply them, but to be strong in theory as well.  Understanding what is happening underneath the code will keep you moving forward. He compares knowing the tools to learning math with a calculator. “If you take the calculator away, you still need to be able to do the work. You need the underlying skills too, so that when you’re in a situation without the calculator, you can still provide solutions.” By constantly striving to collaborate and improve, Tran believes the Data industry has the best chance of innovating successfully.  If you’re looking for a new challenge in an innovative and collaborative environment, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

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