Data Scientist

Green Thumb • Chicago, IL

1d • Hybrid

About The Position

Green Thumb Industries is establishing a data science function to drive operational decisions through demand forecasting, analytics science, and feature engineering. This is a hands-on individual contributor role within a small, high-output, high-visibility team. The position involves building, testing, and maintaining ML models, engineering features, and translating data into actionable business insights. The role reports to the Manager of Data Engineering, AI & ML, who will provide technical direction and business context. The primary focus is to advance existing systems and contribute to their development. This is a hybrid role requiring in-office presence one day every two weeks at the Chicago office.

Requirements

• 2+ years of hands-on experience in a data science, quantitative analyst, or ML engineering role.
• 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).
• Jupyter notebook development or equivalent experience.
• Strong SQL skills, comfortable writing complex queries across multiple joined tables, aggregating at multiple grains, and debugging data quality issues.
• Working experience with supervised and unsupervised ML methods: gradient boosting, time series models, random forest, decision trees, etc.
• Ability to communicate analytical findings clearly in writing.
• Intellectual curiosity and a bias toward figuring things out, navigating real, messy data in a complex multi-state retail operation.
• Must pass any and all required background checks.
• Must be and remain compliant with all legal or company regulations for working in the industry.
• Must be a minimum of 21 years of age.

Nice To Haves

• Experience with time series forecasting methodologies (ARIMA, Prophet, LightGBM/XGBoost for tabular time series, or similar).
• Experience with advanced machine learning modeling techniques and algorithms such as Bayesian inference, Deep Learning neural networks, k-means clustering, etc.
• Familiarity with feature store concepts or structured feature engineering pipelines.
• Exposure to Snowflake, Snowpark, or cloud data warehouse environments.
• Experience with dbt or working in a layered data warehouse (raw → refined → curated).
• Experience prototyping and productionizing data products such as Streamlit apps.
• Basic familiarity with LLM-powered tooling or AI agent frameworks.
• Background in retail, CPG, consumer analytics, or any multi-location operations business.

Responsibilities

• Build, validate, and refine demand forecasting models for GTI's retail, wholesale, and other business verticals across various forecast horizons.
• Engineer new features for the Snowflake Feature Store using retail sales history, inventory movement, weather data, customer demographics, and external signals to enhance model accuracy.
• Develop and test new model candidates against GTI's backtesting framework, interpreting results to inform promotion decisions.
• Investigate forecasting errors and anomalies, identify performance degradation, diagnose root causes, and propose remediation strategies.
• Conduct dimensionality reduction and principal component analysis to understand feature importance.
• Collaborate with the Manager to evolve the feature engineering roadmap, identifying valuable signals, data gaps, and model architectures.
• Design, validate, and execute analytical studies to answer business operational questions, enabling replication by data analyst AI agents for self-service.
• Build reusable analytical frameworks on GTI's curated data layer for repeatable and parameterized use by the business.
• Contribute to quasi-experimental modeling, including pre/post adult-use launch performance, store cohort comparisons, product mix attribution, and discount effectiveness.
• Translate analytical findings into clear written summaries and visualizations for non-technical stakeholders.
• Identify patterns in data that surface new questions for strategy discussions with the Manager.
• Participate in team roadmap and design discussions, contributing an analytical perspective on problem-solving.
• Learn GTI's production data stack (Snowflake, dbt, Dagster) and curated data models.
• Develop familiarity with GTI's Snowflake-based AI agent ecosystem and how structured analytical outputs feed into natural language intelligence tooling.

Benefits

• Competitive pay based on experience, qualifications, and location.
• Eligibility for a discretionary annual incentive program driven by organization and individual performance.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed