Accountabilities:

Data Governance and Standards: Define and establish governed definitions for high-priority derived variables, ensuring consistency and reliability across healthcare datasets and analytical products.
Data Quality Management: Develop quality control rules for each data transformation stage, conduct hands-on profiling of raw and processed datasets, identify anomalies, and investigate their root causes.
Documentation and Compliance: Maintain comprehensive documentation of data pipelines, data models, and transformation logic to ensure transparency, reproducibility, and regulatory defensibility.
Data Product Development: Strengthen analytical assets and data pipelines, transforming prototypes into robust, scalable, and production-ready solutions for client-facing applications.
Engineering Collaboration: Partner with engineering teams to evaluate deliverables against client expectations and quality standards, identify implementation gaps, and coordinate corrective actions.
Workflow and Request Management: Manage shared requests from clients and internal analysts, track progress with engineering teams, and ensure timely resolution of data-related questions and issues.
Data Infrastructure Optimization: Identify opportunities to enhance structured and unstructured data pipelines, prioritize workflows, and improve the generation of valuable data products.
Data Standardization: Accelerate data accessibility by promoting standardized healthcare data models, including OMOP and clinical registries, to support efficient extraction of fit-for-purpose real-world evidence (RWE).
Client Discovery and Prototyping: Participate in discovery sessions with delivery teams and develop prototype dashboards using real-world data, such as patient journey analyses, to translate business needs into technical specifications.
Strategic Client Engagement: Facilitate discussions with biopharmaceutical partners to understand current challenges, anticipate future requirements, and identify opportunities for analytical innovation.
Insights and Recommendations: Present analytical findings, data methodologies, and strategic recommendations to technical teams, business stakeholders, and senior client executives.

Requirements

At least 8 years of experience in data science, advanced analytics, healthcare data, or real-world evidence, preferably supporting biopharmaceutical clients.
Bachelor's degree in a relevant field or equivalent professional experience is required.
Advanced degree in measurement, statistics, epidemiology, or a related discipline is preferred.
Extensive experience working with complex healthcare datasets, including electronic health records (EHR), medical and pharmacy claims, specialty pharmacy dispensing data, laboratory data, and genomic datasets.
Familiarity with patient identity resolution and tokenization solutions, such as Datavant.
Advanced proficiency in SQL and at least one statistical or programming language, such as Python or R.
Experience working with modern data platforms and analytical environments, particularly Databricks.
Strong understanding of healthcare data models, clinical concepts, patient journeys, provider and drug mapping, and real-world evidence methodologies.
Proven ability to identify, investigate, and resolve complex data quality issues and system-level problems using rigorous analytical techniques and root-cause analysis.
Experience translating business and client requirements into clear technical specifications and collaborating with engineering teams to deliver scalable data products.
Strong understanding of data governance, transformation processes, quality assurance, and production pipeline reliability.
Demonstrated ability to influence stakeholders, manage competing priorities, and drive measurable outcomes within complex, matrixed, and cross-functional organizations.
Excellent communication, presentation, and consulting skills, with the ability to explain sophisticated analytical concepts to both technical and executive audiences.
Strong problem-solving abilities, attention to detail, and a structured approach to managing complex data challenges.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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