Director, Applied Intelligence

Role Summary

As Director of Applied Intelligence you will report to the Senior Director Applied Intelligence and lead delivery of rapid high-impact AI/ML prototypes that inform enterprise investment and scaling decisions. You will be a lead on 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.

You will de-risk high-impact AI/ML investments through fast disciplined experimentation. This is not a sandbox. Every proof of concept you produce 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. You will be the first call when something matters and nobody knows the answer yet.

Role Responsibilities

Technical Delivery and Team Leadership

• Lead day-to-day technical delivery of applied AI projects participating in architecture code reviews and prototyping

• Mentor and develop individual contributors set team standards and recruit talent to accelerate delivery

• Maintain hands-on involvement in modeling data pipelines and integration work to preserve technical credibility

Rapid Prototyping and Experimentation

• Run 2 to 6 week prototype cycles: define scope design experiments deliver evaluation metrics and recommend clear go/no-go decisions

• Translate ambiguous commercial problems into testable hypotheses and rapid experiments that produce actionable results

• Remain deeply hands-on in AI/ML engineering and prototyping

Engineering Hygiene and MLOps

• Ensure prototypes follow robust engineering practices: version control CI/CD for models containerization reproducibility automated tests and clear documentation

• Apply MLOps principles for deployment-ready artifacts including model monitoring and rollback strategies appropriate for regulated environments

• Promote modular reusable code and extraction of common patterns to reduce rework

Partnership

• Prepare validated prototypes for structured handoff to industrialization production or IT teams with runnable artifacts runbooks and acceptance criteria

• Collaborate closely with partner Analytics Data Science and IT teams to ensure prototypes align with enterprise architecture and standards

• Support scaled rollouts by providing implementation support during transition from build to operations

Stakeholder Communication

• Communicate prototype results limitations and recommended next steps to the Senior Director product owners and business stakeholders in clear outcome-oriented terms

• Deliver concise executive summaries and data-backed recommendations to inform prioritization and funding decisions

Basic Qualifications

• 8 years of experience in AI/ML data science or applied research with substantial hands-on technical delivery with a BA/BS 7 years with an MS/MBA or 5 years with a PhD

• Active proficiency in Python and modern ML frameworks; regular coding and technical leadership in production or prototype contexts

• Strong practical experience with MLOps model lifecycle management CI/CD concepts containerization and reproducibility tooling

• Demonstrated ability to design and deliver scalable AI/ML solutions while operating in fast resource-constrained prototype environments

• Proven track record applying AI/ML to commercial functions in pharmaceutical or life sciences settings delivering measurable business impact such as improved forecasting segmentation or real-world evidence analytics

• Excellent stakeholder communication skills and experience presenting results and recommendations to senior leaders

• Experience working in 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 related quantitative field

• Prior experience leading prototype teams innovation labs or applied AI delivery teams inside large enterprises

• Familiarity with cloud-agnostic ML deployment patterns and production practices including model deployment monitoring container orchestration and enterprise data governance suitable for regulated environments

• Experience creating reusable engineering patterns templates and runbooks for broader organization use

• Track record of hiring and developing senior individual contributors

Work Location Assignment:Hybrid

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

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.