RWE Data Scientist
Role details
Job location
Tech stack
Job description
As RWE Data Scientist you'll provide a high level of expertise in employing cutting-edge analytical & computational approaches to drive evidence-based pharmaceutical product development; provide scientific and technical leadership in machine learning and AI; work closely with other disciplines across Sanofi including Business Units, Digital, R&D, Biostatistics, Information Technology Systems and other Data Science partners to deliver cutting edge analysis to key business questions.
Examples of Advanced Analytics activities:
- Machine/Deep Learning to elucidate disease trajectories, patient subtypes, define underdiagnosed conditions, and unmet health needs
- Create a framework for generating re-usable models and insights across big-data (e.g. EHRs, claims) and rich small data sets (e.g. clinical trials, imaging)
- Generating insights by merging diverse data streams e.g. health, surveillance, trend data, sensor, imaging;
- Adoption of emerging technology into an analytical framework: distributed analytics, graph databases
People:
- Act as a subject matter expert in machine learning, statistical and/or modelling working on team projects
- Work with internal and external study lead to execute Advance Analytics projects and studies
Performance:
- Implement and execute computational and statistical methodologies in Advanced Analytics for RWE
- Provide expertise and execute advanced analytics for solving problems across R&D, Medical Affairs, HEVA and Market Access Strategies and Plans
Process:
- Apply a broad array of capabilities spanning machine learning, statistics, mathematics, modelling, simulation, text-mining/NLP, data-mining to extract insights and be able to communicate and champion these efforts across the company
- Plan and deploy methodological standards, standardized processes, demos, and POCs for the company's highest priority business needs
- Contribute to the design, development, and implementation of Sanofi's data science architecture and ecosystem to guide decision-making and building foundational capabilities
Requirements
Education: PhD in quantitative field such as Statistics, Biostatistics, Applied Mathematics or related field with 6 years of industry or academic experience; Relevant Master's Degree, with 10 years of related industry or academic experience.
Soft skills: Strong oral and written communication skills; ability to work and collaborate in a team environment
Languages: Excellent knowledge of English language (spoken and written), Better is out there. Better medications, better outcomes, better science. But progress doesn't happen without people - people from different backgrounds, in different locations, doing different roles, all united by one thing: a desire to make miracles happen. So, let's be those people.