Data Scientist, Consumer & Commercial Analytics Made by Gather - the company behind America’s fastest-growing kitchenware brands. Founded in 2003 by entrepreneur Shae Hong, Made by Gather makes super-premium design and innovation accessible through our brands Beautiful by Drew® and bella®. We’ve spent 20+ years developing our special sauce. Summer Fridays Free access to fitness center with lockers and showers in office building Private courtyard Direct access to McGill metro station and REM station Adjacent to Time Out Market and other restaurants and retailers Dynamic environment surrounded by beauty; we provide coffee, drinks, and snacks If you’re looking for a workplace that values initiative, creativity, and results while providing tools to grow through learning and development programs, this is the place for you. About the Role We’re looking for an early‑career data scientist who loves digging into consumer data and turning it into answers. You’ll be the person the team turns to when a question needs more than a dashboard pull, when we need a forecast, a model, a segmentation, or a proper read on whether something caused what we think it did. You’ll work closely with the insights, marketing, sales, demand planning, and product teams, and partner with our data analyst. You’ll be hands‑on with data from day one. This isn’t a “run the weekly report” job. You’ll be close to real commercial decisions: what to launch, what to price, where to advertise, what to cut, and you’ll have room to shape how we use data to answer the questions that move the business. If that sounds like the environment you want, this is the seat. Responsibilities Build analyses that answer real commercial questions: which products are winning, which aren’t, why share is shifting, how media is performing, what consumers are saying, and where the white space is. Develop forecasts, price elasticity models, customer/audience segmentation, and lift/incrementality analyses to support pricing, assortment, innovation, and media planning. Clean, join, and model data from multiple sources into coherent analytical datasets. Apply statistics well: significance testing, regression, basic time‑series work, and knowing when a difference is real vs. noise. Analyze tests and experiments like A/B tests, holdouts, media mix reads, and tell the business what actually worked. Translate findings into clear charts and written takeaways that non‑technical stakeholders can act on. The work isn’t done until someone can use it. Partner with the data analyst so your models and their reports tell the same story. Qualifications 2–3 years of hands‑on data science or advanced analytics experience, ideally with consumer, retail, CPG, or media data. Strong SQL against a cloud warehouse; comfort with warehouse patterns (joins on large tables, window functions, CTEs, query‑cost awareness) matters more than the specific vendor. Strong Python (pandas, numpy; scikit‑learn or statsmodels). Comfort in a notebook environment, querying directly in the warehouse. Solid statistics fundamentals—regression, significance testing, confidence intervals, basic forecasting. You can explain why a method is right for a problem, not just run it. Experience building at least one or two of the following from scratch: forecasts, segmentations, elasticity models, lift/incrementality analyses, or propensity models. Working knowledge of at least one BI/viz tool (Power BI, Tableau, Looker, or similar) for communicating results. Clear written communication. You can turn a model output into a two‑sentence takeaway. Genuine curiosity about consumers and the products they buy. You read reviews for fun. Bonus Qualifications Experience with syndicated data sources (Circana/IRI, Nielsen, NPD, Profitero, GWI, BERA, Yogi, Kantar). Retail media or digital marketing analytics exposure (Amazon Ads, Walmart Connect, Target Roundel, Meta, TikTok). Experience with causal inference methods (diff‑in‑diff, synthetic control, matched‑market tests) or media mix modeling. Familiarity with survey/VOC data—MaxDiff, conjoint, segmentation analysis. Exposure to dbt, version control (git), and modern analytics engineering workflows. #J-18808-Ljbffr