Principal Data Scientist

The Principal Data Scientist is the technical lead for a portfolio of advanced analytics products supporting personalized commercial engagement and performance optimization. Accountable for end-to-end technical strategy, model design, methodology, development, and validation of AI/ML and statistical solutions. Partners with the analytics product owner to translate business outcomes and requirements into rigorous, scalable analytics solutions.

Key Responsibilities:
- Lead advanced analytics capabilities across customer understanding, engagement planning, decision support, and measurement.
- Translate product requirements and business goals into scalable technical solutions (model design, feature engineering, validation, deployment).
- Design predictive and inferential models using complex, multi-source data to generate actionable insights on customer behavior and business performance.
- Evaluate cross-channel engagement patterns, interaction effects, and temporal dynamics.
- Own measurement methodologies to assess effectiveness, incrementality, and business impact (experimental/observational).
- Partner with BTS, Digital Lab, engineering, and platform teams to build scalable, production-ready analytics and AI/ML solutions.
- Establish best practices (code quality, documentation, reproducibility, peer review, version control, methodological rigor).
- Synthesize findings into actionable recommendations for non-technical stakeholders.
- Productionalize AI/ML solutions (deployment, monitoring, lifecycle management).
- Stay current on applied ML, causal inference, and pharmaceutical analytics; pilot emerging methods.

Supervisory/Management:
- No formal direct reports; provides technical mentorship to junior/mid-level data scientists.
- May lead technical workstreams with external analytics vendors/partners.

Key Competencies:
- ML, statistical modeling, causal inference; production-quality analytics delivery.
- Lead technical execution with product owners and cross-functional teams.
- Translate ambiguous business problems into scoped solutions with methods and success metrics.
- Full data science lifecycle (exploration to deployment/monitoring).
- Software engineering discipline (testing, documentation, reproducibility, version control).
- Familiarity with pharmaceutical data ecosystems (HCP, patient, engagement).
- Clear communication to non-technical audiences.
- AI engineering principles (deployment, monitoring, enterprise integration).
- Operationalize solutions at scale across teams.

Qualifications:
- Bachelor’s in Statistics/Math/CS/Engineering or quantitative field (Master’s/PhD preferred).
- 8+ years data science/ML/advanced analytics with production-quality models.
- 5+ years pharmaceutical/biotech/healthcare/life sciences commercial analytics (highly preferred).
- 4+ years omnichannel analytics (journey analysis, touchpoint measurement, optimization, next-best-action).
- 4+ years measuring commercial effectiveness using promo response, closed-loop, A/B testing, quasi-experimental methods.
- Proficiency in Python and/or R; strong SQL; hands-on pandas, NumPy, scikit-learn, PySpark.
- Experience with PyTorch or TensorFlow for sequential/temporal/recommendation use cases.
- Translate requirements with product/business stakeholders.
- Productionize AI/ML in cloud (deployment, monitoring, lifecycle management).

Preferred Experience:
- Advanced ML/optimization (reinforcement learning, multi-armed bandits, transformers).
- Familiarity with IQVIA, Symphony Health, APLD, or similar Rx/claims/engagement datasets.
- Agile product development.
- Cloud ML/MLOps (Azure ML, Databricks, MLflow).
- Present to senior/executive audiences.
- LLM/agentic AI workflows and integration via APIs/pipelines/orchestration.

Benefits:
- Paid time off (vacation, holidays, sick), medical/dental/vision insurance, and 401(k) to eligible employees.
- Eligible for long-term incentive programs.