Data Scientist

The Role

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.

Responsibilities

ML Forecasting

• 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

Analytics Science

• 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

Collaboration & Growth

• Participate in team roadmap and design discussions; contribute your analytical perspective on what problems are worth solving and how

• Learn GTI's production data stack (Snowflake, dbt, Dagster) and the curated data models that underpin all analytical work - these are your primary data surfaces

• Over time, develop familiarity with GTI's Snowflake based AI agent ecosystem and how structured analytical outputs feed into natural language intelligence tooling

Qualifications

• 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

Preferred

• 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) - understanding where data comes from matters here

• Experience prototyping and productionizing data products such as Streamlit apps

• Basic familiarity with LLM-powered tooling or AI agent frameworks - not required, but exposure gives you context for where the team is headed

• Background in retail, CPG, consumer analytics, or any multi-location operations business

Additional Requirements

• 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

Calling all curious, collaborative, compassionate, boundary-pushing, trustworthy stewards: There’s a place for you.

People are everything at Green Thumb. Some of us nurture plants while others pore over legal documents or open new stores. A few things we have in common? We’re stewards of the plant, a happy and humble bunch and genuinely love what we do.