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Overview

Remarcable is purpose-built for trade contractors, helping them manage procurement, tools, and warehouse operations with unmatched visibility and control. As we scale across North America, data is at the core of how we deliver on that promise.

As our Data Scientist, you'll partner closely with Data Engineering to design and build out our data lakehouse, bringing data from across the business into one place to power in-platform and internal reporting, sharpen Remarcable's product intelligence, and surface the trends and next-step recommendations that help contractors and our own teams make better decisions.

This is a high-ownership role for someone who's comfortable wearing multiple hats (analyst, scientist, developer) to shape data architecture and build out reporting, models, and insights that drive action and decision-making.

What You’ll Do

• Partner with Data Engineering to design the lakehouse's semantic and analytical layers (on top of AWS S3, Redshift, Athena, Glue)
• Translate ambiguous business questions into clear analyses, and communicate findings to technical and non-technical stakeholder
• Analyze product, operations, and customer usage data to identify trends, anomalies, and opportunities that inform product and business decisions
• Build models and heuristics that power Remarcable Intelligence (e.g. smart search, recommendation engine, forecasting)
• Build out AI/ML data infrastructure - feature engineering, training datasets, and model evaluation - supporting SageMaker and Bedrock workflows
• Develop and maintain core analytics supporting company metrics (ARR, churn, NRR, product usage, ROI)
• Create customer-facing and internal reporting for customers and internal teams
• Design and validate product experiments (A/B tests and other measurement methodologies) to drive data-informed decisions that improve user experience and business outcomes

Who You Are

• 5+ years of experience in a Data Scientist, Analytics, or similar role
• Someone who thrives in a startup - adaptable, resourceful, and motivated by impact.
• Strong SQL skills: complex queries, window functions, performance tuning
• Proficiency in Python (pandas, NumPy)
• Experience building dashboards/reporting (e.g., QuickSight, Looker, Tableau, Power BI, or custom-built reporting)
• Experience with modern data transformation tools (e.g., dbt).
• Experience building, validating, and deploying practical ML and heuristic models to solve business problems in a production setting (e.g. recommendation, trend analysis, scoring).