Data Scientist

This is a hands-on Data Scientist role focused on demand forecasting, feature engineering, and analytics to support multi-location retail operations. You will build, test, and maintain ML models, engineer features, and translate data into actionable insights for non-technical stakeholders. You will work closely with the Manager of Data Engineering, AI & ML and contribute to a growing, production-focused data science function.

• Build, validate, and refine demand forecasting models across daily, weekly, monthly, and quarterly horizons for retail, wholesale, and emerging business channels.
• Engineer features for a Snowflake-based feature store using retail sales history, inventory movement, weather, customer demographics, and external signals.
• Develop, backtest, and compare new model candidates using the client’s established backtesting framework; interpret results to inform inventory and promotion decisions.
• Diagnose forecasting errors and anomalies (data drift, structural breaks, new store openings, regulatory changes) and propose remediation plans.
• Apply dimensionality reduction and PCA to identify primary feature importance.
• Design and execute analytical studies that answer operational business questions and enable repeatable, parameterized frameworks.
• Build reusable analytical frameworks on top of curated data layers (retail sales, inventory, customer, loyalty, workforce) to promote self-service.
• Contribute to quasi-experimental analyses: pre/post launch performance, store cohort comparisons, product mix attribution, and discount effectiveness.
• Translate analytical findings into clear written summaries and visualizations for business stakeholders.
• Participate in roadmap and design discussions, helping prioritize signals, data gaps, and model architectures to explore.
• Learn and work with the production data stack (Snowflake, dbt, Dagster) and related AI tooling over time.

• 2+ years of hands-on experience in data science, quantitative analysis, or ML engineering with demonstrable work in model building, feature engineering, or statistical analysis.
• Strong Python skills for data manipulation, modeling, and analysis (pandas, scikit-learn, statsmodels, or equivalent). Experience developing in Jupyter or similar.
• Strong SQL skills — comfortable authoring complex queries, aggregating at multiple grains, joining tables, and debugging data quality issues.
• Working experience with supervised and unsupervised ML methods (gradient boosting, time series models, random forest, decision trees, etc.).
• Clear written communication skills to explain analytical findings and recommended actions to non-technical stakeholders.
• Intellectual curiosity and a bias toward figuring things out in messy, multi-state retail data environments.
• Bachelor’s degree or equivalent experience.

• Experience with time series forecasting methods (ARIMA, Prophet, LightGBM/XGBoost for tabular time series, or similar).
• Familiarity with advanced ML techniques (Bayesian inference, deep learning, clustering).
• Experience with feature store concepts or structured feature engineering pipelines.
• Exposure to Snowflake, Snowpark, cloud data warehouses, and dbt or layered data warehouse patterns (raw → refined → curated).
• Experience prototyping or productionizing data products (Streamlit, dashboards, lightweight apps).
• Basic familiarity with LLM-powered tooling or AI agent frameworks.
• Background in retail, CPG, consumer analytics, or multi-location operations.

• The pay range is $90,000—$115,000 USD, dependent on experience, qualifications, and/or location of the role.
• Positions may be eligible for a discretionary annual incentive program driven by organization and individual performance.
• Benefits and additional compensation details will be provided by the confidential client during the hiring process.

• Hybrid — requires in-office presence approximately 1 day every 2 weeks at an office in River North, Chicago, IL.

• Employment may be contingent upon successful background checks and any industry-required screenings.
• Must meet industry-specific legal or regulatory requirements to work in the role; where applicable, candidates must be at least 21 years of age in accordance with industry and state regulations.
• Applicants may be required to demonstrate authorization to work in the United States; the confidential client will comply with applicable employment and immigration laws.
• This job posting is intended to comply with applicable U.S. federal, state, and local employment laws.
  

CareerTakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.

If you have questions about how your data is used, please contact us directly.