Associate Director, Data Science – Commercial

Gilead Sciences

. Bring innovative data science techniques and insights to business to drive and support strategic business decisions .

Posted 10/13/2026full-timeUnited StatesSenior💰 $195,670 - $253,220 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning algorithms and data science techniques, with a strong focus on predictive modeling and the application of AI-based solutions in the pharmaceutical sector. Proven ability to lead data science projects, manage cross-functional teams, and communicate complex insights effectively to stakeholders.

Highest-signal resume keywords

Machine Learning AlgorithmsData Science TechniquesPython ProgrammingPharma Datasets ExpertiseLeadership and Team Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPredictive ModelingData AnalysisStatistical MethodsAI-Based SolutionsData Science LibrariesEconometric ModelingNeural NetworksRegression AnalysisClustering

Soft Skills

Effective CommunicationTeam CollaborationCoaching and MentoringInclusive MindsetSolution-Oriented Approach

Tools & Technologies

PythonDatabricksS3NumpyPandasScikit-LearnSeabornNetworkXIQVIASymphony

Industry Keywords

Pharmaceutical DataPatient ClaimsCommercial MarketData Science ProductsPatient JourneyOmnichannel AnalyticsHealth Care ApplicationsData Science ModelsCross-Functional TeamsQuantitative Field

Tech Stack

Tools & technologies

CloudNumpyPandasPythonScikit-Learn

About the role

Key responsibilities & impact

Bring innovative data science techniques and insights to business to drive and support strategic business decisions
Establish integrated HCP, patient journey and predictive sciences capabilities at scale for the commercial market
Work closely with integrated insights, commercial operations, patient engagement and omnichannel analytics leads
Drive industrialization of predictive sciences to understand patient journeys and market triggers
Develop and deploy scalable data science products and insights influencing marketing, sales and medical decisions
Lead data science projects end to end, translating unstructured business questions into data science solutions
Guide offshore teams while coding and debugging hands-on
Communicate with stakeholders and abstract backend complexity when appropriate
Foster a culture of measurement and impact and continuously improve data science models
Apply predictive algorithms and methodologies to construct robust propensity models for commercial use
Create refreshable, reproducible and replicable data science products
Define retraining schedules and measure propensity model impact with Data Scientists and Analysts
Partner with global teams to cross-pollinate ideas and replicate successful models across countries
Manage data science initiatives and deliveries for a priority therapeutic area
Build, develop and motivate teams through hiring, coaching and mentoring
Create inclusion, develop talent and empower teams

Requirements

What you’ll need

Bachelor's Degree and Eight Years' Experience OR Master's Degree and Six Years' Experience OR PhD and Two Years' Experience
Strong working knowledge of machine learning algorithms, including regression, clustering, neural networks, Bayesian models, RNN, CNN, tree-based algorithms, SMOTE and related methods
Experience building, implementing and using AI-based solutions with proven business impact
Experience working with pharma and consumer data types and sources such as patient claims, Xponent, Plantrak, sales and activity data
Strong leadership and ability to manage initiatives from beginning to end
Effective written and verbal communication skills
Expertise in pharma datasets such as IQVIA, Symphony, Komodo claims, Optum, Definitive Health, Health Verity and EMR/EHR
Expertise in Python and data science libraries including numpy, pandas, scikit-learn, seaborn and networkx
Expertise in data science techniques including ANCOVA, Bayesian statistics, econometric modeling and neural networks/logistic models
Understanding of cloud-based technologies and tools such as Databricks and S3
Demonstrated product mindset
Familiarity with product management principles
Strong team player with an inclusive, objective, cross-functional and solution-oriented mindset
Understanding of emerging data science capabilities and their potential application in pharma/health care
Thorough understanding of datasets, including capture rates, projections and acceptable error ranges
Master's degree in a quantitative field with at least 8 years of relevant data science/analytics experience OR undergraduate degree in a quantitative field with at least 10 years of relevant data science/analytics experience

Benefits

Comp & perks

Discretionary annual bonus
Discretionary stock-based long-term incentives
Paid time off
Company-sponsored medical insurance
Company-sponsored dental insurance
Company-sponsored vision insurance
Company-sponsored life insurance