Lead implementation of AI models and tools - including ML, NLP, LLM, and intelligent automation - to enhance decision-making, strengthen risk controls, and drive operational efficiency.
Define success metrics and instrumentation, run controlled experiments, monitor model drift, and enable rollback/kill-switches and human-in-the-loop workflows to ensure reliability and safety.
Ensure Responsible AI practices by embedding governance, explainability, fairness, privacy, and compliance into every stage of product development - from model documentation and validation to audit trails.
Stay ahead of emerging AI/ML trends and regulatory developments, conducting build/buy/vendor assessments in collaboration with InfoSec, Third-Party Risk, and Compliance teams.
Communicate product vision and strategy clearly to executives, stakeholders, and clients, influencing direction at a strategic and organizational level.
Establish and manage the operating rhythm for AI products, including weekly scorecards, monthly and quarterly business reviews, and OKR tracking.
Own the AI product metrics taxonomy and performance dashboards, ensuring all KPIs and success metrics are measurable, actionable, and aligned with business outcomes.
Prepare concise executive readouts and narrative memos that translate data into decisions - highlighting priorities, funding needs, and resourcing recommendations, with clear escalation of risks or issues.
Drive data quality for reporting by defining event schema, validating telemetry, and partnering with analytics teams to resolve data gaps and improve instrumentation integrity.
Requirements
Bachelor’s degree in Computer Science, Engineering, Finance, or related field (or equivalent experience); advanced degrees or AI certifications are a plus.
10+ years of product management experience, with 3+ years building or scaling AI/ML or intelligent automation products in production.
Demonstrated ability to create strategic roadmaps and ship iteratively across multiple scrum teams in a highly matrixed environment.
Working knowledge of ML/LLM concepts (data pipelines, model evaluation, prompt/RAG design, human‑in‑the‑loop, MLOps/monitoring).
Metrics & storytelling fluency - experience building executive dashboards, setting OKRs, and turning data into clear recommendations; basic SQL or BI tooling literacy (e.g., Tableau/Power BI/Looker) is a plus.
Experience managing daily technical and design direction in partnership with Engineering, Data Science, and Design.
Excellent stakeholder management, communication, and decision‑making skills; proven track record of driving innovation and leading through change.
Benefits
Access to flexible global resources and tools
Focus on your health
Foster personal resilience
Reach financial goals
Generous paid leaves
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
AI modelsMLNLPLLMintelligent automationdata pipelinesmodel evaluationMLOpsSQLBI tooling
Soft skills
stakeholder managementcommunicationdecision-makingstrategic roadmap creationinnovationleading through changeinfluencingdata storytellingcollaborationproblem-solving
Certifications
Bachelor’s degree in Computer ScienceBachelor’s degree in EngineeringBachelor’s degree in FinanceAI certifications