Staff Data Scientist, Epidemiology and Patient Data Products

🌍 Remote, USA 💹 Full-time 🕐 Posted Recently

Job 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.

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.

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