Sr Biostatistician-Sr Data Scientist/Analyst OMOP (US and UK Only)

Sr Biostatistician-Sr Data Scientist/Analyst OMOP (US and UK Only)

Syneos Health is a leading fully-integrated life sciences services organization built to accelerate customer success. We partner with innovators at every point across the drug development and commercialization continuum, helping them navigate complexity, anticipate change and accelerate progress.

Our Clinical Solutions team members act with a drug development mindset, applying their years of experience and deep expertise to truly understand customer needs and represent those in the solutions we shape.

Whether you join us in a Functional Service Provider partnership or a Full-Service environment, you'll collaborate with passionate problem solvers, innovating as a team to help our customers achieve their goals. We are agile and driven to deliver - for one another, our customers, and, most importantly, for those in need.

Discover what your 25,000 future colleagues already know:

Why Syneos Health

• We are passionate about developing our people, through career development and progression; supportive and engaged line management; technical and therapeutic area training; peer recognition and total rewards program.
• We are committed to building an inclusive culture - where you can authentically be yourself. Central to this is our purpose - Driven to Deliver - which captures the passion of our colleagues to show up each day and shape solutions that have the ability to dramatically impact someone's life.
• We are continuously building the company we all want to work for and our customers want to work with. Why? Because we know that when we bring together smart colleagues from across the world, we can shape the future of healthcare, driving impact for customers and defining the pace of patient progress.

Job Responsibilities

• Must be located in the United States or UK with no sponsorship needs to be considered for this position.

Key Responsibilities

OHDSI / DARWIN Analytics & Implementation

Collaborate with researchers to understand project requirements and translate them into OHDSI/DARWINcompatible solutions. Build, validate, and execute OMOP CDM cohorts and analyses using:ATLAS (cohort definition, characterization, exploration) ATHENA (vocabulary browsing, concept sets) ACHILLES (data characterization and quality insights) R packages in the OHDSI/DARWIN ecosystem (for cohort execution, characterization, estimation, and related workflows). Perform analyses using ATLAS and/or Prometheus or via programmatic workflows in R (and Python where appropriate), depending on study needs and platform patterns. Customize and extend OHDSI tools and applications as needed to support project- or portfolio-specific requirements.

Observational Study Design, Statistics, and AI/ML Awareness

Apply and document observational study designs and epidemiologic concepts for RWE (e.g., descriptive epidemiology, cohort designs, self-controlled designs, comparative approaches as applicable). Implement and interpret appropriate statistical methods, including confounding control strategies, time-to-event approaches, sensitivity analyses, and fit-for-purpose evaluations. Maintain familiarity with machine learning and statistical concepts to support exploratory modeling, feature engineering, prediction workflows (when relevant), and method selection.

Reproducible, AI-Enabled, Production-Grade Analytics

Develop reusable and maintainable analytic code, prioritizing reproducibility and auditability (clear methods, parameterization, and structured outputs). Incorporate AI-assisted workflows (nice to have and can be taught) to improve efficiency in:Code generation/refactoring Validation and QA checks Documentation and study write-ups Exploratory analysis and summary generation while ensuring results remain transparent, traceable, and scientifically defensible.

GitHub-Centric Collaboration

Use GitHub extensively (branching, pull requests, code reviews, issue tracking) to deliver collaborative, production-grade analytics.

Required Qualifications

Masters degree in Statistics or related field. Demonstrated experience with OMOP CDM and OHDSI tooling, including ATLAS (or Prometheus), ATHENA, and ACHILLES. Proficiency in common OHDSI community languages: SQL and R. Python is good too but most open-source tooling is in R Strong understanding of observational study design and epidemiologic concepts, with emphasis on RWE. Experience working with healthcare data such as EHR and insurance claims, including healthcare data standards and fit-for-purpose evaluation. Solid understanding of clinical terminologies such as SNOMED, ICD-9/10, CPT, HCPCS, READ (and related standard vocabularies used in OMOP). Experience with data quality assessment and data validation techniques. Proven ability to work in a fast-paced environment, delivering high-quality outputs with strong documentation and collaboration. Strong problem-solving ability and comfort working in cross-functional teams. Excellent commun