Associate Director, Commercial AI – Business Value Delivery

Job Description

Associate Director Commercial AI Business Value Delivery Digital Human Health

Job Description

This role sits within the Commercial AI vertical of Digital Human Health (DHH) which is responsible for building and scaling AI capabilities across cross-functional teams and divisions. The team operates as an embedded strategic partner to the commercial organization providing thought leadership and delivering AI-powered solutions to marketing and cross-functional stakeholders. It shapes demand by translating business priorities into scalable AI products and agent-driven solutions that enhance decision-making and drive measurable business impact.

Role Overview

As the Associate Director Commercial AI Business Value Delivery this role is accountable for shaping and delivering AI and Agentic AI solutions across Commercial functions and DHH value teams (Marketing Market Access Field Force Effectiveness Omnichannel and Forecasting).

This role combines strong commercial analytics leadership with AI/GenAI technical literacy to frame business problems build value cases align stakeholders and drive adoptionpartnering closely with product data science engineering IT and risk/control teams.

The roles primary mandate is measurable value realization: defining success metrics enabling scalable deployments and ensuring solutions are governed safe and fit for enterprise use.

Key Responsibilities

• Frame business problems as clear decision statements with defined value hypotheses success metrics (ROI KPIs) and value realization cadence.
• Lead commercial analytics strategy across enterprise use cases for agentification (segmentation NBE forecasting omnichannel marketing effectiveness field insights HCP journey) using a business-first evidence-led approach.
• Translate priorities into a value-driven AI roadmapsequenced by impact feasibility and riskwith defined scope success criteria and adoption plans.
• Define AI product vision including problem statement user personas workflows guardrails and success metrics (operational business impact).
• Own stakeholder alignmentinfluence senior leaders manage trade-offs and drive cross-functional/global accountability.
• Drive adoption and change management through operating model design (incl. human-in-the-loop) training communication and sustained usage tracking.
• Establish portfolio governance for Commercial AI (intake prioritization value-based sequencing delivery checkpoints risk/compliance alignment).
• Ensure measurable value delivery via early metric definition MVP/pilot validation post-launch tracking and continuous improvement loops.
• Scale and standardize capabilities by building reusable assets playbooks and cross-market solutions.
• Mentor and lead engineers/data scientists: design reviews coding standards coaching hiring input and building a high-performing delivery culture.

Technical Expertise

• Partner with tech leads and engineering to shape AI/Agentic AI solution approaches (LLM workflows RAG patterns tool/function calling orchestration; multi-agent patterns only when appropriate and governed).
• Convert business needs into well-formed requirements and user stories; ensure the design supports reliability scalability maintainability and cost controls (e.g. performance/latency expectations and run-cost awareness).
• Ensure enterprise deployment practices are applied: LLMOps/MLOps CI/CD monitoring/observability evaluation frameworks drift/quality gates and operational readiness for production.
• Establish guardrails and responsible AI controls: hallucination mitigation data privacy-by-design security considerations auditability model risk awareness documentation and compliance alignment for a regulated environment.
• Review and challenge analytical and GenAI approaches (internal and vendor) for rigor transparency explainability and compliance; drive corrective actions where needed.
• Support vendor/partner evaluation (build vs buy) including technical due diligence proof-of-concept design and recommendations based on time-to-value scalability ownership and risk.

Education Requirements

Bachelors/Masters (or equivalent) in Computer Science Artificial Intelligence Data Science Machine Learning Statistics Engineering or a related quantitative discipline with strong focus on AI/ML and modern data systems.

Required Experience and Skills

• 8 years of relevant experience across software engineering commercial analytics ML engineering and AI product engineering and delivery with hands-on experience in production deployments at scale.
• 4 years leading teams and driving delivery across multiple stakeholders; proven ability to mentor and raise engineering standards.
• Experience driving AI/GenAI adoption in a regulated or high-compliance environment including privacy auditability and model risk management.
• Familiarity with commercial data domains such as CRM promotional digital engagement and related performance measurement.
• Experience with experimentation design measurement approaches and operationalizing performance measurement at scale.
• Prior experience assessing AI solution build and managing team and stakeholder delivery with strong governance.
• Exposure to operating model design for AI products (intake-to-scale lifecycle governance enablement playbooks).
• Demonstrated capability to establish reusable frameworks/SDKs integration patterns and scalable operating models for AI delivery.
• Excellent stakeholder communication skills with the ability to explain trade-offs risks and outcomes clearly to technical and non-technical audiences.

Required Skills:

Preferred Skills:

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