Data Scientist

The RoleGreen Thumb Industries is building a data science function that powers real operational decisions demand forecasting that drives inventory positioning, analytics science that surfaces what's happening in our stores, and feature engineering that makes every model smarter over time.This is a hands-on individual contributor role on a small, high-output, high-visibility team. You will spend your time building, testing, and maintaining ML models, engineering features, and translating data into answers that the business can act on. You will work closely with the Manager of Data Engineering, AI & ML, who will guide your technical direction and business context while you grow into shaping both. The systems are already starting to get built your job is to push them further.This is a hybrid role and requires in office work 1 day per week every 2 weeks at our office in River North in downtown Chicago.ResponsibilitiesML ForecastingBuild, validate, and refine demand forecasting models for GTI's retail, wholesale, and other emerging business verticals across daily, weekly, monthly, and quarterly forecast horizonsEngineer 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 dimensionsDevelop and test new model candidates against GTI's established backtesting framework; interpret backtest results and surface findings to inform promotion decisionsInvestigate forecasting errors and anomalies: identify when model performance degrades, diagnose root causes (data drift, structural breaks, new store openings, regulatory changes), and propose remediationConduct dimensionality reduction and principal component analysis to understand primary feature importanceCollaborate with the Manager to evolve the feature engineering roadmap identifying signals worth building, data gaps worth closing, and model architectures worth exploringAnalytics ScienceDesign, validate, and execute analytical studies that answer business-users operational questions which can then be modeled and replicated by our data analyst AI agent to further promote self-serviceBuild 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 businessContribute to quasi-experimental modeling: pre/post adult-use launch performance, store cohort comparisons, product mix attribution, and discount effectivenessTranslate analytical findings into clear written summaries and visualizations that non-technical stakeholders can act onIdentify patterns in the data that surface new questions worth asking and bring those to strategy discussions with the ManagerCollaboration & GrowthParticipate in team roadmap and design discussions; contribute your analytical perspective on what problems are worth solving and howLearn GTI's production data stack (Snowflake, dbt, Dagster) and the curated data models that underpin all analytical work these are your primary data surfacesOver time, develop familiarity with GTI's Snowflake based AI agent ecosystem and how structured analytical outputs feed into natural language intelligence toolingQualifications2+ 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 analysisStrong Python skills for data manipulation, modeling, and analysis (pandas, scikit-learn, statsmodels, or equivalent). Jupyter notebook development or equivalent experienceStrong 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 trustWorking experience with supervised and unsupervised ML methods: gradient boosting, time series models, random forest, decision trees, etcAbility to communicate analytical findings clearly in writing you don't just run the analysis, you explain what it means and what to do about itIntellectual curiosity and a bias toward figuring things out this role requires navigating real, messy data in a complex multi-state retail operationPreferredExperience 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, etcFamiliarity with feature store concepts or structured feature engineering pipelinesExposure to Snowflake, Snowpark, or cloud data warehouse environmentsExperience with dbt or working in a layered data warehouse (raw ? refined ? curated) understanding where data comes from matters hereExperience prototyping and productionizing data products such as Streamlit appsBasic familiarity with LLM-powered tooling or AI agent frameworks not required, but exposure gives you context for where the team is headedBackground in retail, CPG, consumer analytics, or any multi-location operations businessAdditional RequirementsMust pass any and all required background checksMust be and remain compliant with all legal or company regulations for working in the industryMust be a minimum of 21 years of age#LI-HYBRIDThe pay range is competitive and based 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.Green Thumb Pay Range$90,000 - $115,000 USDSalary $90000 USD per year recblid tjq5o4stxhjmb3tju6k0y7u6j8u1nl