Manager, Data Science and Advanced Analytics

Philip Morris International•Stamford, CT

13h•Hybrid

About The Position

Be a part of a revolutionary change! At Philip Morris International (PMI), we’ve chosen to do something incredible. We’re totally transforming our business and building our future on one clear purpose – to deliver a smoke-free future. With huge change, comes huge opportunity. So, if you join us, you’ll enjoy the freedom to dream up and deliver better, brighter solutions and the space to move your career forward in endlessly different directions. Our success depends on people who are committed to our purpose and have an appetite for progress. Our beautiful HQ in Stamford, CT is just steps away from the Stamford Metro-North Train Station and easily accessible from NYC.

Requirements

Legally authorized to work in the U.S.
Commutable distance to Stamford, CT.
Bachelor’s degree in Data Science, Statistics, Computer Science, Business Analytics, or a related field (Master’s preferred).
7+ years of experience in data analytics or data science roles, ideally within CPG, retail, or similarly data-rich environments.
Proficiency in SQL and experience with Python or R for data analysis.
Hands-on experience with BI tools such as Power BI or Tableau.
Familiarity with structured datasets (e.g., sales, customer, or syndicated data) and common analytics use cases.
Strong problem-solving skills and ability to manage multiple priorities in a fast-paced environment.
Effective communication skills, with the ability to present insights to non-technical stakeholders.

Responsibilities

Partner with stakeholders to support analytics initiatives that align to key business priorities across commercial, sales, and operations functions.
Design and deliver analytics projects, including descriptive analysis, segmentation, forecasting, and basic predictive modeling.
Build and maintain dashboards, reports, and data visualizations using tools such as Power BI or Tableau.
Query, manipulate, and analyze large datasets using SQL and Python (or similar tools) to generate actionable insights.
Ensure data accuracy and consistency by applying best practices in data validation and transformation.
Translate analytical findings into clear, business-friendly insights and recommendations.
Collaborate cross-functionally with business teams, data engineers, and senior analytics leaders to support decision-making.