Senior Biostatistician – Senior Data Scientist/Analyst, OMOP
Job Description:
• Collaborate with researchers to understand project requirements and translate them into OHDSI/DARWIN‑compatible 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
• 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
• Develop reusable and maintainable analytic code, prioritizing reproducibility and auditability (clear methods, parameterization, and structured outputs)
• Incorporate AI-assisted workflows 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
• Use GitHub extensively (branching, pull requests, code reviews, issue tracking) to deliver collaborative, production-grade analytics
Requirements:
• 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
• 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 communication skills, with the ability to convey technical concepts to technical and non-technical stakeholders
• Strong experience developing and maintaining documentation for RWE studies and insight generation
• Demonstrated willingness and ability to incorporate AI tools into workflows for efficiency and quality
Benefits:
• company car or car allowance
• Health benefits to include Medical, Dental and Vision
• Company match 401k
• eligibility to participate in Employee Stock Purchase Plan
• Eligibility to earn commissions/bonus based on company and individual performance
• flexible paid time off (PTO)
• sick time
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• Collaborate with researchers to understand project requirements and translate them into OHDSI/DARWIN‑compatible 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
• 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
• Develop reusable and maintainable analytic code, prioritizing reproducibility and auditability (clear methods, parameterization, and structured outputs)
• Incorporate AI-assisted workflows 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
• Use GitHub extensively (branching, pull requests, code reviews, issue tracking) to deliver collaborative, production-grade analytics
Requirements:
• 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
• 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 communication skills, with the ability to convey technical concepts to technical and non-technical stakeholders
• Strong experience developing and maintaining documentation for RWE studies and insight generation
• Demonstrated willingness and ability to incorporate AI tools into workflows for efficiency and quality
Benefits:
• company car or car allowance
• Health benefits to include Medical, Dental and Vision
• Company match 401k
• eligibility to participate in Employee Stock Purchase Plan
• Eligibility to earn commissions/bonus based on company and individual performance
• flexible paid time off (PTO)
• sick time
Apply tot his job
Apply To this Job