- Company Name
- Gilead Sciences
- Job Title
- Director, Data Science - Measurement & Optimization
- Job Description
-
**Job Title**
Director, Data Science – Measurement & Optimization
**Role Summary**
Lead the design, development, and deployment of advanced measurement models and resource‑optimization solutions for pharma commercial markets. This individual‑contributor role drives data‑driven insights that inform marketing, sales, and medical strategies, connecting business stakeholders with analytical solutions and overseeing an offshore data science team.
**Expectations**
- Own end‑to‑end data science projects from problem framing to stakeholder communication.
- Apply cutting‑edge statistical, econometric, and machine‑learning techniques to deliver actionable, repeatable models.
- Champion a measurement culture, ensuring ROI, engagement, and lift metrics are consistently tracked.
- Remain current with digital and AI developments (e.g., Generative AI) and benchmark industry best practices.
**Key Responsibilities**
1. Design and implement marketing‑mix, resource‑allocation, and exposure‑measurement models (ANCOVA, Bayesian, econometrics, neural nets, etc.).
2. Design and execute experiments (A/B, multivariate) to validate tactics and adjust strategies.
3. Define and monitor KPI frameworks (ROI, lift, engagement) to assess model impact.
4. Translate business questions into scalable data‑science products and communicate results to cross‑functional teams.
5. Lead, coach, and collaborate with offshore data scientists; debug code, review outputs, and ensure reproducibility.
6. Maintain model lifecycle: retraining schedules, propensity scoring, and impact assessment.
7. Cross‑pollinate ideas with global teams, adapt models to new regions, and share best practices.
**Required Skills**
- Advanced knowledge of machine‑learning algorithms: regression, clustering, neural nets, Bayesian, RNN, CNN, tree‑based (RF, XGB, LightGBM), SMOTE, etc.
- Proficiency in R and/or Python, SQL, and data‑pipeline orchestration tools.
- Strong statistical foundation in predictive and causal modeling.
- Experience building AI/ML solutions that deliver measurable business impact.
- Leadership in independent project ownership, mentorship, and stakeholder management.
- Excellent written and verbal communication; ability to abstract technical details.
**Required Education & Certifications**
- Bachelor’s degree or higher in Statistics, Applied Mathematics, Computer Science, Data Science, or related field (Master’s or PhD preferred).
- Relevant certifications (e.g., Certified Analytics Professional, Microsoft Certified: Azure Data Scientist Associate, or equivalent) are advantageous.