Manager Data Scientist
$104,000–$130,000 year
HybridTampa, Florida, United States
Job Summary
Design, build, and validate predictive and prescriptive models addressing key business problems such as demand forecasting, pricing elasticity, and promotion effectiveness. Own analytics initiatives end-to-end, translating ambiguous business questions into structured problems, developing models, deploying solutions, and monitoring performance against real-world feedback. Partner with data engineering and platform teams to operationalize models into production workflows and embed outputs into planning cycles and commercial decision processes. Communicate analytical approaches, assumptions, and limitations clearly to non-technical stakeholders, framing outputs as scenarios and trade-offs. Develop reusable analytical assets and frameworks to reduce one-off effort while ensuring proper documentation and auditability across the organization.
Required Qualifications
- Bachelor's degree in Statistics, Mathematics, Economics, Data Science, Engineering, or related field
- 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
Desired Qualifications
- Master's degree (preferred)
- Experience in CPG, Retail, or Commercial Analytics environments
- Hands-on work in areas such as: Pricing & promotion effectiveness
- Hands-on work in areas such as: Demand forecasting
- Hands-on work in areas such as: Revenue Growth Management (RGM)
- Hands-on work in areas such as: 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
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