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# Data Science Manager

- **Company:** Starbucks
- **Location:** Watertown, TN, United States
- **Experience:** Expert
- **Salary:** $146,400.0 - $244,300.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Business Analytics Applications, Data Analysis, Microsoft Azure, Cloud Database, Data Security, Relational Databases, Distributed Computing Environment, R (Programming Language), Python (Programming Language), Machine Learning, Systems Development Life Cycle, SQL Databases, Information Technology, Software Coding, Software Version Control, Databricks
- **Published:** August 5, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2908745796-2

## About the Role

* BA/BS (or equivalent experience)
 * 5-7+ years of experience in data science, analytics, or a closely related field
 * Proficiency with Python or R, SQL, and relational databases
 * Working knowledge of machine learning/statistical techniques (eg, regression, decision trees, classification, causal inference, clustering)
 * Experience with cloud data/analytics solutions (eg, Azure or AWS)
 * Experience managing and coaching a team, with strong written/verbal communication skills and attention to detail, * MS in a quantitative field (eg, Statistics, Math, Computer Science, Engineering, Economics, Psychology, Quantitative Social Science) or equivalent advanced experience
 * 8+ years managing a small team (approximately 2-4 people) and developing technical talent
 * Experience with production ML practices (shared codebases, package creation, repositories, SDLC best practices), plus distributed processing/modern platforms (eg, Databricks or similar)
 * Experience with hyperparameter tuning and ongoing model performance monitoring (measurement, drift, stability)

## Description

* Lead and develop a high-performing team by coaching, creating development plans, and building a culture of strong execution, collaboration, and continuous learning.
 * Drive end-to-end data science delivery from problem framing and data preparation through modeling, performance assessment, and operationalization of insights/models.
 * Set and scale best practices across coding standards, version control, documentation, data security, and analytics/engineering practices-removing roadblocks and aligning cross-functional partners.


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