Director Data Science and Advanced Analytics

Philip Morris International U.S.•Stamford, CT

1d•Hybrid

About The Position

The Director, Advanced Analytics will lead the strategy, execution, and adoption of advanced analytics across the enterprise, shaping how predictive analytics, machine learning, and AI are applied to drive growth, productivity, and operational excellence.

Requirements

Legally authorized to work in the U.S.
Commutable distance to Stamford, CT.
10+ years of experience in analytics, data science, or decision science, with 5+ years leading teams and portfolios delivering production‑ready solutions.
Bachelor’s degree in Data Science, Statistics, Computer Science, Economics, Operations Research, or a related field (Master’s or PhD a plus).
Proven track record delivering predictive analytics, ML, or AI solutions with measurable business impact.
Strong people‑leadership capabilities, including hiring, mentoring, performance management, and team development.
Solid hands‑on understanding of modern analytics and ML techniques, experimentation design, and measurement approaches; able to engage credibly with data scientists and engineers.
Familiarity with modern analytics platforms and ecosystems (e.g., Databricks, Snowflake, cloud data platforms) and model operationalization practices (MLOps, CI/CD, monitoring).
Experience establishing or maturing analytics standards, governance, and model lifecycle management practices.

Nice To Haves

Master’s or PhD a plus

Responsibilities

Own the advanced analytics portfolio and roadmap, defining a standardized approach to generating insights and decision products. Prioritize initiatives based on value, feasibility, and appropriate risk or regulatory considerations, partnering with business leaders on outcomes rather than just deliverables.
Build, lead, and mentor a high‑performing team spanning data science, decision science, analytics engineering, and insight translation. Establish a culture of curiosity, accountability, and continuous learning with clear expectations and development paths.
Lead the development and deployment of predictive models, machine‑learning solutions, and AI‑enabled analytics that improve decision quality, automation, forecasting, and personalization.
Establish consistent analytics and model lifecycle practices across experimentation, development, deployment, and monitoring. Where appropriate, leverage modern platforms and tooling (e.g., Databricks‑based workflows and MLOps practices) to ensure reproducibility, scalability, and reliability.
Partner closely with Data Engineering, Data Architecture, and Data Product Management to ensure high‑quality, curated, and governed data and features are available to support advanced analytics and production use cases.
Act as a senior advisor to stakeholders—translating ambiguous business questions into analytic hypotheses, measurement plans, and actionable recommendations. Embed enablement and change management to drive adoption.
Implement and uphold standards for model documentation, validation, explain‑ability, fairness and bias considerations, monitoring, and value tracking in partnership with IT, Security, and Compliance.
Quantify business impact (e.g., revenue uplift, margin improvement, cost‑to‑serve reduction, forecast accuracy gains) and communicate outcomes clearly to executive and non‑technical audiences