Manager Data Scientist

Philip Morris International U.S.•Tampa, FL

18h•$104,000 - $130,000•Hybrid

About The Position

We are seeking a Manager, Data Science (Advanced Analytics) to design and deliver high‑impact predictive and prescriptive analytics solutions that directly inform business decisions. This role is a hands‑on advanced analytics practitioner, not focused on descriptive reporting, dashboard development, or KPI tracking. Instead, it requires deep expertise in statistical modeling, forecasting, machine learning, and analytical problem solving, with a proven ability to translate complex business problems into scalable, decision‑ready solutions. You will operate as a technical owner of analytics use cases, partnering with business, product, and data platform teams to take solutions from problem framing through deployment and ongoing optimization. While this role may provide guidance or mentorship to junior team members, it is primarily an individual contributor role with accountability for outcomes, not team size.

Requirements

Bachelor’s degree in Statistics, Mathematics, Economics, Data Science, Engineering, or related field (Master’s preferred).
5–8+ years of experience in data science or advanced analytics roles with demonstrated hands‑on modeling experience.
Proven track record of delivering model‑driven analytics, not just reporting or dashboards.
Strong proficiency in Python and/or R, with practical application of statistical and machine learning methods.
Experience with forecasting, regression, classification, and/or optimization models
Experience working with large, complex, imperfect datasets (e.g., retail scan, consumer panels, transactional data)
Ability to translate analytics into business decisions and measurable outcomes.

Nice To Haves

Experience in CPG, Retail, or Commercial Analytics environments.
Hands‑on work in areas such as: Pricing & promotion effectiveness, Demand forecasting, Revenue Growth Management (RGM), Consumer behavior analytics
Experience with cloud data platforms (e.g., Snowflake, Databricks).
Familiarity with deploying and operationalizing models in production environments.
Ability to balance accuracy, interpretability, and business usability.

Responsibilities

Design, build, and validate predictive and prescriptive models addressing key business problems (e.g., demand forecasting, pricing elasticity, promotion effectiveness, consumer behavior).
Apply appropriate techniques including time‑series forecasting, regression, classification, optimization, or simulation based on business need.
Select modeling approaches grounded in business context, data constraints, interpretability, and scalability, not theoretical novelty.
Own analytics initiatives from problem framing → data exploration → model development → deployment → performance monitoring.
Translate ambiguous business questions into structured analytical problems with clear success criteria.
Continuously refine models based on real‑world performance and stakeholder feedback.
Partner with data engineering and platform teams to ensure models move beyond proof‑of‑concept into production or repeatable business workflows.
Embed outputs into planning cycles, commercial decision processes, and operational routines.
Monitor model performance, identify drift, and recommend enhancements or retirement as needed.
Communicate analytical approaches, assumptions, and limitations clearly to non‑technical stakeholders.
Frame outputs as scenarios, trade‑offs, and business implications, not just model results.
Act as a trusted analytics partner to business stakeholders.
Develop reusable analytical assets, frameworks, and modeling components to reduce one‑off effort.
Ensure proper documentation, explainability, and auditability of models.
Contribute to analytics standards and best practices across the organization.