Associate Director Data Scientist, Customer Experience & IT

AstraZeneca

Associate Director, Data Scientist at AstraZeneca applying machine learning and analytics for commercial strategy. Leading data-driven decision making and mentoring junior professionals in Japan.

Posted 7/22/2026full-timeOsaka • 🇯🇵 JapanSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in advanced analytics, machine learning, and epidemiology to drive evidence-based decision-making in the pharmaceutical industry. Proficient in building and deploying models using real-world data to optimize commercial strategies and enhance stakeholder communication.

Highest-signal resume keywords

Machine Learning Model DevelopmentReal-World Data AnalysisEpidemiological MethodsCross-Functional Project LeadershipBusiness-Level Proficiency in Japanese and English

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Advanced AnalyticsMachine LearningEpidemiologyForecasting ModelsData Pipeline DesignDeep LearningNatural Language ProcessingStatistical AnalysisPerformance MonitoringGap Analysis

Soft Skills

Analytical ThinkingStrong CommunicationLeadership CapabilityEffective CollaborationStrategic Mindset

Tools & Technologies

MDVJMDCNDBDPCPublic Health Data SourcesAnalytical PlatformsVisualization ToolsVersion Control SystemsProject Management ToolsData Science Community Engagement

Industry Keywords

PharmaceuticalHealthcareLife SciencesCommercial AnalyticsMarket AccessBrand StrategyPatient Journey AnalysisPost-Marketing SurveillanceValue DemonstrationCare Pathway Optimization

About the role

Key responsibilities & impact

Responsible for applying advanced analytics, machine learning, epidemiology, and real-world data methods to support AstraZeneca Japan’s commercial PDCA cycle and evidence-based decision-making
Builds and maintains models for territory planning, HCP targeting, performance monitoring, sales target setting, new patient forecasting, gap analysis, corrective action recommendations, and evaluation of commercial interventions
Leads root cause analysis to explain performance gaps across HCPs, facilities, regions, channels, and time periods
Leverages Japan-specific real-world data sources such as MDV, JMDC, NDB, and DPC, together with public health and policy data, to generate insights for market shaping, brand strategy, patient journey analysis, care pathway optimization, HTA, value demonstration, and post-marketing surveillance
Develops forecasting and scenario simulation models that incorporate epidemiology, market dynamics, competitive activity, policy signals, and pricing assumptions
Designs analytical pipelines and platforms that integrate structured data, RWD, and public information for scalable machine learning applications
Promotes modern methods such as deep learning, NLP, agentic AI, and parallel computing, while ensuring model quality, reproducibility, version control, monitoring, and retraining
Partners with brand teams, market access, medical affairs, business excellence, IT, external vendors, and analytics partners to translate business questions into analytical solutions, manage project delivery, and communicate insights through clear visualization, documentation, and storytelling
Contributes to capability building by mentoring junior professionals, promoting analytical best practices, and supporting the CET data science community

Requirements

What you’ll need

7+ years of experience in data science, machine learning, or quantitative analytics, with substantive project delivery
5+ years of experience in pharmaceutical, healthcare, or life sciences industry strongly preferred (consulting experience in pharma commercial analytics also valued)
Demonstrated experience building and deploying ML models in a production environment
Demonstrated experience working with real-world data (claims, EHR, registry data), epidemiological methods, and public health data sources
Experience leading cross-functional analytical projects with both business and IT stakeholders
Experience managing project scope, schedule, and outcome quality across multiple parallel workstreams
Experience managing senior stakeholder expectations to maximise value for both business partners and the analytics team
Strong analytical thinking with ability to translate complex business questions into rigorous analytical frameworks
Solid business acumen with understanding of pharmaceutical commercial dynamics, brand lifecycle, and market access
Strong communication skills — able to explain complex analytical concepts to non-technical stakeholders through clear documentation and visualisation
Proven leadership capability — able to mentor junior team members and lead small project teams
Strategic mindset with the ability to prioritise high-impact opportunities and manage detailed tasks across multiple parallel projects
Effective collaborator across business, IT, and external vendor relationships
Business-level proficiency in Japanese and English

Benefits

Comp & perks

AstraZeneca embraces diversity and equality of opportunity
Inclusion and diversity are fundamental to the success of our company
Committed to building an inclusive and diverse team