Staff Data Scientist, Epidemiology and Patient Data Products

Description: • As a Staff Data Scientist in Epidemiology and Patient Data Products, you will be a core member of a team of data scientists advancing the discovery and development of new medicines. • In this role, you will answer research questions using large real world healthcare databases to inform identification of biological molecules for effective drug development under the guidance of epidemiology program leads. • You will work in partnership with colleagues in machine learning, statistical genetics, and computational biology to develop solutions to challenging computational problems. • Successful candidates will work with a diverse set of scientists and domain experts and engage with external partners, in ways that cut across traditional industry boundaries in an innovative startup environment. • Lead real world data studies (e.g., electronic medical records) from end-to-end to generate causal evidence for projects in drug discovery and development. • Translate research questions into observational study designs to generate patient-centric insights from statistical models. • Curation of clinical and non-clinical variables for machine learning models • Execution of trajectory modeling techniques using real world data • Interpreting machine learning results into patient profiles. • Executing post-hoc longitudinal analyses among patient profiles of interest • Be comfortable with scientific uncertainty and embrace curiosity and creative solutions. • Work with a diverse array of data spanning electronic medical records, sequencing, multi-omics data, and other data modalities using R and Python in cloud environments. • Use your technical knowledge and intuition to articulate and break down large problems into solvable pieces. There are a lot of problems to solve; you’ll need to prioritize which of these are critical-path today from those that can wait. • Collaborate with drug discovery and clinical development teams to help ensure the relevance and impact of the insights generated by you and your teammates. • Be a dynamic and active team member, championing and adopting shared coding standards, participating in code review, and providing regular updates of your work and input into the work of your colleagues • MPH, MS with 5+ years or PhD in epidemiology or biostatistics with 3+ years of work-related experience applying epidemiological, statistical, and/or machine learning methods to real-world datasets. • Must have 3+ years of experience developing and executing robust analytical strategies, including cohort and case control study design, using health care databases including electronic health records, administrative claims databases, and/or patient registries. • Experience leading epidemiologic projects from end-to-end: from translating research questions into observational study designs, contrasting strengths and weaknesses of different study designs and statistical approaches, and generating patient-centric insights from statistical models. • Extensive experience with causal approaches applied to observational studies, including propensity score methods, bias adjustment, and covariate selection and adjustment. • Advanced knowledge in biostatistics approaches, including inferential and predictive modeling, and comfortable implementing unsupervised machine learning algorithms in real world health care databases. • Must have experience conducting data manipulation and statistical analysis in Python and/or R programming languages. • Comfortable working in ambiguous problem spaces; experience working in a start-up or agile work environment as part of cross-functional project teams. • Ability to lead and facilitate meetings and work collaboratively on multi-disciplinary project teams. • Exceptional time management, ability to prioritize multiple tasks simultaneously, and deliver products on time every time. • Enthusiastic about documentation–ensuring that all analyses are clear and reproducible with thorough documentation of key assumptions and decision points. • Research experience in obesity, cardiometabolic, and/or neurodegenerative therapeutic areas. • Experience developing and maintaining machine learning pipelines, and translating machine learning output into meaningful insights for diverse audiences is a plus. • Familiarity with or exposure to traditional drug discovery and development processes and approaches is a plus. • Hands-on experience curating structured health data and working in health data from outside of the U.S. Requirements: • MPH, MS with 5+ years or PhD in epidemiology or biostatistics with 3+ years of work-related experience applying epidemiological, statistical, and/or machine learning methods to real-world datasets. • Must have 3+ years of experience developing and executing robust analytical strategies, including cohort and case control study design, using health care databases including electronic health records, administrative claims databases, and/or patient registries. • Experience leading epidemiologic projects from end-to-end: from translating research questions into observational study designs, contrasting strengths and weaknesses of different study designs and statistical approaches, and generating patient-centric insights from statistical models. • Extensive experience with causal approaches applied to observational studies, including propensity score methods, bias adjustment, and covariate selection and adjustment. • Advanced knowledge in biostatistics approaches, including inferential and predictive modeling, and comfortable implementing unsupervised machine learning algorithms in real world health care databases. • Must have experience conducting data manipulation and statistical analysis in Python and/or R programming languages. • Comfortable working in ambiguous problem spaces; experience working in a start-up or agile work environment as part of cross-functional project teams. • Ability to lead and facilitate meetings and work collaboratively on multi-disciplinary project teams. • Exceptional time management, ability to prioritize multiple tasks simultaneously, and deliver products on time every time. • Enthusiastic about documentation–ensuring that all analyses are clear and reproducible with thorough documentation of key assumptions and decision points. Benefits: Apply tot his job Apply To this Job