Data Science Specialist

Mandatory: pharma/biotech/healthcare domain exp Experienced consultant for real-world data and AI initiatives in the pharmaceutical and healthcare sectors, bringing end-to-end expertise ranging from use-case definition through prototyping and validation to production-ready handover to internal teams. Strong focus on evidence-based decision-making, compliance, scalability, and measurable business impact. Key Expertise:Strategy & Roadmaps: Development of DS&AI strategies, use-case portfolios, and value-case assessments; prioritization based on impact, feasibility, and risk; definition of KPI/OKR frameworks.RWD Methodology: Study and analysis design for health services research (unmet need, patient pathways, outcomes) based on routine health insurance data, EMR/EHR, registry and claims data, PROs/PREMs; data evaluation and linkage.Advanced Analytics & ML: Design and review of classification, regression, prediction, and time-series models; NLP/LLM, deep learning, recommender systems; bias/fairness assessments, reproducibility, and audit readiness.Omnichannel Enablement: Consulting on promotional mix optimization, next best action, segmentation/targeting, experimental design (A/B testing), and impact measurement.MLOps/DataOps Consulting: Roadmap for ML production readiness, CI/CD for ML, feature stores, model registry, monitoring (drift, performance), documentation, and handover standards for internal operations teams.Data & Vendor Due Diligence: Evaluation of external data sources and providers, technical due diligence, data protection/compliance checks (e.g., GDPR), licensing/usage models, and cost-benefit analyses.Stakeholder Alignment: Facilitation between Medical, Market Access, Payer/Governmental Affairs, and Marketing/Sales; workshops, storylining, decision-making documents; Training/Enablement for functional departments.Service Package (Project-Based)Discovery & Scoping: Problem definition, hypotheses, data requirements, risk/ethics assessment; roadmap and MVP plan.Prototyping & Validation: Development of PoCs/MVPs including success criteria, statistical validation, and reproducibility; documentation for traceability.Operationalization: Architecture and operations consulting, handover to internal teams (runbooks, monitoring KPIs, quality checklists); support through go-live.Governance & Compliance: Establishment of review gates, model risk management, bias/fairness frameworks, data protection concepts; preparation for internal/external audits.Capability Building: Training, playbooks, templates, code and dashboard guidelines; coaching for product owners and analysts.Technology Stack (consulting-ready)Languages/Tools: Python, R, SQL; review of production code, pipelines, and notebooks.Cloud & BI: AWS (S3, Athena, SageMaker), Git, CI/CD, Power BI; Architecture and tool selection.Data formats/models: FHIR, OMOP, claims/billing structures; pseudonymization/de-identification.Skills & QualificationsIndustry focus: 6+ years in pharma/biotech/healthcare or consulting with measurable DS&AI results; experience with RWD and study data.Communication: Presenting complex analyses in a way tailored to the audience; decision-making documents, executive storyboarding, stakeholder workshops.Work Style: Independent, structured, high-performing; clear prioritization in dynamic settings; reliable in meeting deadlines and quality targets.Languages: Very good command of German and English