Data Science Lead

Job Summary

The Associate Director, Data Science Lead will serve as an authoritative analytical partner driving the strategic design, deployment, and scaling of advanced artificial intelligence and machine learning architectures across commercial operations. By leveraging longitudinal patient data (APLD), advanced predictive modeling, and scalable MLOps environments, this role directly influences strategic decision-making in Marketing, Sales, and Market Access. The successful candidate will bridge high-level commercial vision with rigorous quantitative methodologies to optimize patient engagement, streamline market access, and ultimately accelerate the delivery of novel therapies to target patient populations.

Responsibilities

• Strategic AI/ML Leadership: Architect and execute long-term commercial data science roadmaps aligned with enterprise AI/ML trends, translating commercial objectives into scalable machine learning frameworks.
• Advanced Model Development: Lead the end-to-end engineering, deployment, and continuous optimization of predictive models, natural language processing (NLP), clustering, and optimization algorithms to solve complex commercial challenges.
• Patient-Centric Analytics: Harness longitudinal claims data (APLD), PlanTrak, specialty pharmacy data, and real-world evidence (RWE) to develop patient-level predictive models that improve therapeutic access and field force effectiveness.
• Cross-Functional & Technical Integration: Partner directly with Commercial Leadership, Insights, and Brand teams to ensure insights are operationalized; collaborate closely with IT to build and sustain robust MLOps ecosystems for productized analytics solutions.
• Governance & Quality Assurance: Establish rigorous model governance, validation frameworks, and continuous fine-tuning mechanisms, maintaining high quantitative standards and responsible experimentation protocols.
• Vendor & Partner Management: Oversee strategic relationships with external analytics providers, ensuring data engineering alignment, security, regulatory compliance, and seamless execution.

Qualifications

• Experience: Minimum 7+ years of hands-on data science and quantitative analytics experience, with at least 4+ years leading complex data science projects or technical teams within pharma, biotech, or life sciences commercial environments.
• Technical Expertise: Proven command of advanced statistical modeling, supervised/unsupervised machine learning algorithms, time-series forecasting, and A/B testing methodologies.
• Data & Tech Stack: Advanced proficiency in Python, SQL, Databricks, and Snowflake, with direct hands-on experience utilizing complex healthcare datasets (APLD, PlanTrak, claims, specialty pharmacy data).
• Execution & Communication: Track record of deploying productized data science models into active commercial workflows and effectively translating analytical outputs for executive leadership.