Director, Data Science - Measurement & Optimization

Role Summary

Director, Data Science - Measurement & Optimization at Gilead, based in Parsippany, NJ. Reports to the Senior Director, Data Science – AI & Optimization. You will apply advanced data science techniques and insights to drive strategic business decisions, with accountability for measurement solutions and resource optimization across commercial markets. This is an individual contributor role supported by a team of offshore data scientists, with occasional travel to global locations.

Responsibilities

• Lead the development and delivery of models and methodologies to inform and evaluate brands' marketing and sales tactics (e.g., marketing mix, resource allocation) using the latest data science techniques (ANCOVA, Bayesian Statistics, Econometrics, Neural Networks/Logistic, etc.)
• Oversee and guide the development and execution of experiments (e.g., A/B and multi-variate tests) to assess the effectiveness of tactics, modifying initiatives as required
• Lead the design of KPIs to track the effectiveness of recommendations that have been implemented and the measurement of campaign impact (e.g., ROI, engagement, lift)
• Understand Gilead's commercial business objectives, develop and deploy scalable data science products and insights to influence decisions in marketing, sales, medical, etc.
• Lead Data science projects end to end including converting unstructured business questions into data science solutions, give guidance to offshore, be a hands-on leader who can code and debug, and communicate with stakeholders.
• Foster a culture of measurement and impact and incorporate feedback to continuously improve data science models
• Bring thought leadership and thorough understanding of statistics, primarily predictive algorithms & methodologies, to construct robust propensity models for impactful commercial use
• Create data science products that can be refreshed, reproduced and replicated consistently
• Work with other Data Scientists and Analysts to define retraining schedule and measure propensity models for impact
• Partner with global teams to cross-pollinate ideas and replicate successful models from other countries and vice versa
• Translate complex data concepts into actionable insights and communicate with stakeholders

Qualifications

• Required: Bachelor's Degree and Twelve Years' Experience OR Masters' Degree and Ten Years' Experience OR PhD and Eight Years' Experience
• Required: Strong working knowledge of machine learning algorithms, including regression, clustering, neural networks, Bayesian models, RNN, CNN, Tree-based algorithms (RF, XGB, LightGBM), SMOTE, etc.
• Required: Experience in building, implementing and using AI-based solutions with proven business impact
• Required: Strong leadership capable of managing initiatives from beginning to end independently
• Required: Benchmarking experience in digital and / or emerging AI fields such as generative AI
• Preferred: Experience in measuring, implementing, optimizing and using AI-based solutions to establish proven business impact
• Preferred: Experience in designing test control experiments
• Preferred: Experience working with standard pharma and consumer data types and sources such as patient claims, Xponent, Plantrak, sales, activity
• Preferred: Expertise in commonly used pharma datasets such as IQVIA, Symphony, Komodo claims, Optum, Definitive health, Health Verity, EMR/EHR
• Preferred: Expertise in Python including commonly used data science libraries such as numpy, pandas, scikit-learn, seaborn, networkx, etc.
• Preferred: Expertise in data science techniques such as ANCOVA, Bayesian Statistics, Econometric modelling, Neural Networks/Logistic, etc.
• Preferred: Understanding of cloud-based technologies and tools such as Databricks, AWS, etc.
• Preferred: Experience designing measurement solutions in any visualization software (Tableau preferred)
• Preferred: Experience with ex-US (European) markets is not required but highly preferred
• Preferred: Demonstrated product mindset
• Preferred: Familiarity with product management principles
• Preferred: Strong team player. Inclusive, objective, cross-functional, team member with a positive and solution-oriented mindset
• Preferred: Understanding of emerging data science capabilities (fields, methodologies, algorithms, etc.) and potential application in pharma/health care
• Preferred: Thorough understanding of datasets including their strengths and limitations such as capture rate, projections and acceptable error ranges for different therapeutic spaces

Skills

• Machine learning, statistics, and data science techniques including regression, clustering, neural networks, Bayesian methods, econometric modeling, and related tools
• Programming and libraries: Python (numpy, pandas, scikit-learn, seaborn, networkx, etc.)
• Experience with AI-based solutions and demonstrable business impact
• Ability to translate data science concepts into business terms and communicate with stakeholders

Additional Requirements

• Occasional travel to global Gilead locations may be required