Data Scientist

Green Thumb Industries is building a data science function that powers real operational decisions. The Data Scientist will focus on building, testing, and maintaining machine learning models, while translating data into actionable insights for the business.

What you'll do:

• Build, validate, and refine demand forecasting models for GTI's retail, wholesale, and other emerging business verticals across daily, weekly, monthly, and quarterly forecast horizons
• Engineer new features for the Snowflake Feature Store — drawing from retail sales history, inventory movement, weather data, customer demographics, and external signals — to improve model accuracy across store, product, market and other dimensions
• Develop and test new model candidates against GTI's established backtesting framework; interpret backtest results and surface findings to inform promotion decisions
• Investigate forecasting errors and anomalies: identify when model performance degrades, diagnose root causes (data drift, structural breaks, new store openings, regulatory changes), and propose remediation
• Conduct dimensionality reduction and principal component analysis to understand primary feature importance
• Collaborate with the Manager to evolve the feature engineering roadmap — identifying signals worth building, data gaps worth closing, and model architectures worth exploring
• Design, validate, and execute analytical studies that answer business-user’s operational questions which can then be modeled and replicated by our data analyst AI agent to further promote self-service
• Build reusable analytical frameworks on top of GTI's curated data layer (retail sales, inventory, customer, loyalty, workforce) that can be repeated, parameterized, and handed off to the business
• Contribute to quasi-experimental modeling: pre/post adult-use launch performance, store cohort comparisons, product mix attribution, and discount effectiveness
• Translate analytical findings into clear written summaries and visualizations that non-technical stakeholders can act on
• Identify patterns in the data that surface new questions worth asking — and bring those to strategy discussions with the Manager
• Participate in team roadmap and design discussions; contribute your analytical perspective on what problems are worth solving and how

What they're looking for:

• 2+ years of hands-on experience in a data science, quantitative analyst, or ML engineering role — 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). 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 in query output, while validating accuracy and trust
• 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 — you don't just run the analysis, you explain what it means and what to do about it
• Intellectual curiosity and a bias toward figuring things out — this role requires 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
• 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

Benefits:

• Positions may be eligible for a discretionary annual incentive program driven by organization and individual performance.