Senior Director, Applied Intelligence

As Senior Director of Applied Intelligence you will lead a high-velocity elite team purpose-built to do one thing exceptionally well: take the hardest most ambiguous AI/ML problems and rapidly determine whether they are solvable how they should be solved and what it will take to make them real at scale. This role will be focused on Commercial use cases and will sit on the Data Science Leadership Team within Global Commercial Analytics.

You will build and lead an internal team that de-risks high-impact AI/ML investments through fast disciplined experimentation. This is not a sandbox. Every proof of concept your team produces will be built with the engineering hygiene of production code because the best prototypes become the foundation of enterprise systems. Your work will directly shape what gets scaled what gets killed and where the organization invests next.

You will operate at the intersection of AI innovation engineering discipline and business urgency. Your team will be the first call when something matters and nobody knows the answer yet.

Role Responsibilities

Technical Leadership and Hands-On Delivery

• Serve as the technical anchor of the team by actively contributing to architecture code and experimental design; you model the standard not just set it

• Remain deeply hands-on in AI/ML and software engineering ensuring your leadership is grounded in the reality of the work

• Attract develop and retain a focused team of exceptional applied AI engineers and data scientists who thrive in ambiguous fast-moving environments

Rapid Prototyping and Experimentation

• Own a 2 to 6 week rapid prototype cycle framework: scope decisively experiment fast validate against clear success criteria and surface actionable go/no-go recommendations

• Serve as the organizations go-to group for rapid AI/ML prototyping the team senior leaders call when they have a high-value problem and no clear path forward

• Design and run structured experimentation that converts ambiguous business questions into testable hypotheses and validated AI/ML approaches

• De-risk high-impact high-ambiguity problems through fast disciplined iteration before committing to large-scale investment

Engineering Excellence

• Enforce strict engineering hygiene in every prototype: version control GitHub-based workflows CI/CD practices reproducibility modular code automated testing and documentation are non-negotiable standards regardless of stage

• Champion containerization and MLOps frameworks as foundational elements of even early-stage work

• Ensure every prototype is built to be understood reproduced and extended by teams who did not build it

• Balance execution speed with engineering discipline shipping fast without creating technical debt that forecloses future scaling

Organizational Leverage and Standards

• Extract reusable patterns templates and best practices from successful prototypes and codify them into shared assets accessible to the broader Data Science organization

• Lead the responsible and effective adoption of Generative AI tools building internal capability at scale while establishing guardrails appropriate for a regulated pharmaceutical environment

• In collaboration with partner Analytics Data Science AI and Digital teams define publish and champion engineering and MLOps standards that elevate technical maturity across the organization

• Promote reusable modular testable code as the cultural and operating standard for AI/ML development

Partnership

• Partner closely with broader Data Science teams and industrialization teams to execute structured well-documented handoffs of validated solutions ready for scaling operationalization and ongoing maintenance

• Design prototypes with the downstream consumer in mind; success is defined by a downstream teams ability to own and scale the solution not simply by prototype functionality

• Collaborate constructively with Digital without creating shadow infrastructure or parallel processes and ensure prototype environments are designed for eventual enterprise integration

Executive Communication and Strategic Influence

• Translate complex AI/ML prototype outcomes into clear actionable narratives for senior leadership and executive stakeholders

• Provide honest evidence-based recommendations on scaling decisions including when not to scale

• Represent the AI Solutions function as a strategic voice in Data Science leadership helping shape the long-term AI/ML agenda for the organization

Basic Qualifications

• 12 years of progressive experience in AI/ML data science or applied research with a demonstrated track record of technical leadership 11 years with an MS/MBA or 9 years with a PhD

• Active hands-on technical proficiency in Python and modern ML frameworks (e.g. PyTorch scikit-learn TensorFlow LangChain); you write code regularly and intend to keep doing so

• Deep expertise in MLOps frameworks ML lifecycle management and production-grade model development practices

• Strong command of software engineering fundamentals including CI/CD pipelines containerization (Docker/Kubernetes) Git-based workflows testing frameworks and reproducibility tooling

• Demonstrated experience designing scalable AI/ML architectures while operating effectively in lean fast-moving prototype environments

• Proven ability to lead through ambiguity by taking ill-defined problems defining the right question and structuring a disciplined path to an answer quickly

• Experience building and deploying Generative AI applications including LLM-based systems prompt engineering and retrieval-augmented generation (RAG) architectures

• Strong executive communication skills with the ability to write a concise one-pager that drives C-suite decisions and present prototype outcomes with clarity and conviction

• Experience working in or with regulated industries such as pharmaceutical biotech medical devices or financial services

Preferred Qualifications

• Advanced degree (M.S. or Ph.D.) in Computer Science Statistics Computational Biology Engineering or a related quantitative discipline

• Proven track record applying AI/ML to commercial functions in pharmaceutical or life sciences organizations delivering measurable business impact (for example: forecasting customer segmentation pricing or real world evidence analytics).

• Prior experience structuring and leading a rapid prototyping innovation lab or AI center of excellence function within a large enterprise

• Experience with cloud based machine learning and enterprise data architectures including model deployment monitoring containerization and data governance in regulated environments.

• Experience authoring or contributing to internal engineering standards best practice guides or center of excellence frameworks

• Track record of hiring developing and retaining exceptional senior individual contributors

Candidate demonstrates a breadth of diverse leadership experiences and capabilities including: the ability to influence and collaborate with peers develop and coach others oversee and guide the work of other colleagues to achieve meaningful outcomes and create business impact.
Relocation support available

Work Location Assignment:Hybrid

Last Date to Apply for Job: 5/8/2026

Relocation assistance may be available based on business needs and/or eligibility.

Candidates must be authorized to be employed in the U.S. by any employer.

U.S. work visa sponsorship (such as TN O-1 H-1B etc.) is not available for this role now or in the future.