Offer summary

Qualifications:

Doctorate degree (PhD, PharmD, or MD) with 5 years of relevant experience, or a Master's degree with 8 years, or a Bachelor's degree with 10 years of experience., Strong background in data sciences and AI/ML methodologies., Proven leadership skills with experience in building and mentoring high-performing teams., Ability to translate complex data insights into actionable business strategies..

Key responsibilities:

Architect and refine the enterprise AI-enabled forecasting vision across various business functions.

Prototype and implement advanced data models using cutting-edge techniques.

Lead and scale a data science team while ensuring high engineering standards.

Collaborate with cross-functional teams to integrate governance and ethical considerations into data models.

Job description

Career Category

Scientific

Job Description

As a Director of Data Sciences, You will be responsible for:

Responsibilities

Architect the enterprise AI-enabled forecasting vision—design and continuously refine a roadmap that spans operations, finance, supply-chain, and commercial planning.
Prototype and productionize advanced models using probabilistic time-series, deep learning, causal inference, reinforcement learning, agent-based simulation, and generative/agentic techniques.
Lead and scale a high-caliber data-science team—recruit, mentor, and establish engineering standards while remaining personally hands-on during early build-out.
Implement robust ML Ops & data pipelines (CI/CD, version control, monitoring, automated retraining) on modern cloud platforms and distributed compute environments.
Partner cross-functionally with Responsible AI, security, compliance, privacy, and domain SMEs to embed governance, fairness, and privacy-by-design into every model.
Translate insights into action—deliver clear storytelling, visualizations, and decision frameworks that drive executive alignment and measurable business impact.
Continuously scan the frontier of AI/ML research, vendor tooling, and open-source innovation to keep the organization at the cutting edge.

Accountabilities

Forecast accuracy & reliability—demonstrated uplift vs. baselines and transparent communication of uncertainty.
On-time delivery of forecasting products and scenario analyses that inform strategic and operational decisions.
Compliance with internal model-risk governance and applicable biotech regulations, with documented audit trails and controls.
Scalable, maintainable technical architecture that supports future growth in data volume, use cases, and team size.
Talent development & retention, creating a learning culture and clear career pathways for data scientists and engineers.
Comprehensive performance monitoring—KPI dashboards, model drift detection, and continuous-improvement cycles.
Stakeholder satisfaction across business units, evidenced by adoption rates and feedback scores.

Qualifications

Doctorate degree PhD OR PharmD OR MD [and relevant post-doc where applicable] and 5 years of [Job Code’s Field and/or Sub-Discipline] experience
OR
Master’s degree and 8 years of [Job Code’s Field and/or Sub-Discipline] experience
OR
Bachelor’s degree and 10 years of [Job Code’s Field and/or Sub-Discipline] experience

Competencies

Strategic Systems Thinking —connects complex data ecosystems to enterprise value creation.
Technical Curiosity & Rigor —relentless pursuit of innovative methods and high engineering standards.
Inclusive, Empowering Leadership —builds diverse, psychologically safe teams that outperform.
Influence & Storytelling —distills sophisticated analytics into compelling narratives and pragmatic recommendations.
Change Agility —navigates ambiguity, pivots quickly, and guides partners through AI-driven transformation.
Integrity & Responsible AI Mentality —champions ethical, transparent, and privacy-preserving model development.
Results Orientation —holds self and team accountable for delivering measurable impact on critical business objectives.