Data Scientist Associate

Best Buy-posted 3 days ago
Full-time • Entry Level
Richfield, MN
5,001-10,000 employees
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We at Best Buy work hard every day to enrich the lives of customers through technology, whether they come to us online, visit our stores or invite us into their homes. We do this by solving technology problems and addressing key human needs across a range of areas, including entertainment, productivity, communicating with coworkers and loved ones, preparing nutritious food, providing security for your home and family, and helping you take your health to the next level. As a Data Scientist Associate , you will drive changes for the Workforce business team. Your role impacts our labor strategy and adds value through innovative data capabilities and insights to optimize our labor planning. We seek candidates with experience to solve problems in forecasting, optimization and operations. A strong candidate will have a background in a diverse range of data science and optimization research techniques and a strong understanding of its application in the retail industry

  • Collect, clean, and preprocess large-scale datasets from BigQuery, Teradata, and other sources to create reliable features.
  • Develop hypotheses and conduct exploratory data analysis to discover key predictors and drivers of workforce outcomes.
  • Design, train, validate, and optimize forecasting, scheduling, and labor cost models balancing productivity and fairness metrics.
  • Conduct rigorous experimentation and causal analysis (A/B testing) to evaluate labor interventions’ impact on sales and employee satisfaction.
  • Build and maintain dashboards and reports tracking labor KPIs and model performance
  • Partner with MLOps teams to prepare models for deployment, ensure version control, enable experiment logging, and implement performance monitoring and incident response protocols.
  • Communicate complex model designs, assumptions, and business impact effectively to stakeholders to support data-driven labor planning and decisions.
  • Bachelor’s degree in a quantitative field (Data Science, Statistics, Computer Science, Engineering, Mathematics, Operations Research, etc.).
  • 2+ years of relevant professional experience in analytics, data science, or a closely related field (or equivalent experience).
  • 2+ years of experience using Python and SQL (e.g., BigQuery, Teradata) for data wrangling, analysis, and modeling, including hands-on use of core data science libraries such as NumPy, Pandas, SciPy, scikit-learn, and statsmodels.
  • 2+ years of experience applying statistics, data analysis, and quantitative modeling to diverse business problems; able to design, validate, and clearly communicate predictive, prescriptive, and descriptive models.
  • 2+ years of working knowledge of mathematical optimization and operations research; able to formulate and solve decision-making problems (e.g., resource allocation, scheduling, task assignment, inventory, routing) by translating business constraints into mathematical models.
  • 2+ years of experience with time-series forecasting methods and libraries (e.g., Prophet, statsmodels) and applying them to real business use cases.
  • 2+ years of experience with Git/GitHub or similar version-control systems for collaborative development, code review, and reproducibility.
  • 1+ year of experience with cloud ML pipeline orchestration (e.g., Kubeflow on GCP), including CI/CD, artifact and metadata tracking, monitoring execution, and logging model/system metrics.
  • 1+ years of experience with Bash or other shell scripting for workflow automation and data pipelines.
  • Advanced experience with Python data manipulation and distributed computing libraries such as Polars and Dask.
  • Proficiency with machine learning frameworks beyond the core stack, including XGBoost, LightGBM, CatBoost, PyTorch, and TensorFlow/Keras.
  • Strong grounding in advanced statistical and time-series techniques, including hypothesis testing, ANOVA, ARIMA/SARIMA, ETS, bootstrapping, and regression diagnostics.
  • Expertise in mathematical optimization using solvers such as Gurobi, OR-Tools, PuLP, Pyomo, and CPLEX, with strong knowledge of linear programming, mixed-integer programming (MIP), and duality theory.
  • Deep domain knowledge of retail, workforce, and supply chain operations.
  • Experience with Google Cloud Platform tools including Vertex AI for managing the end-to-end machine learning lifecycle (data preparation, model training, hyperparameter tuning, deployment, and monitoring); familiar with Vertex AI Workbench, AutoML for automated model building, Vertex AI Pipelines for workflow orchestration, Model Registry for managing models, and integration with BigQuery and Cloud Storage for scalable data engineering and analytics.
  • Proficiency in Business intelligence tools: Power BI, Looker, Looker Studio.
  • Backend API development: FastAPI, Flask for prediction and optimization services integrated with operational tools.
  • Master's degree in a quantitative field (Data Science, Statistics, Computer Science, Engineering, Math, Operations Research, etc).
  • Competitive pay
  • Generous employee discount
  • Physical and mental well-being support
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