Data Scientist

About the Role

The Data Scientist is a technical analytics role supporting Finance, Operations, Marketing, and Development with data and business analysis. The majority of the role is hands-on coding and analysis: writing SQL against a Snowflake data warehouse, building Python-based analytical models, and developing BI dashboards. The role covers the full spectrum from descriptive analytics (what happened) to predictive modeling (what will happen) to prescriptive analysis (what should we do about it).

A Day in the Life

• Write SQL and Python to build, test, and maintain analytical datasets and pipelines in Snowflake.
• Build descriptive analytics: dashboards, KPI frameworks, and ad hoc queries that answer "what happened and why."
• Build predictive models: forecasting, segmentation, and other statistical/ML models that answer "what's likely to happen."
• Build prescriptive analysis: scenario modeling and recommendations that answer "what should we do."
• Partner with Finance, Ops, Marketing, and Development stakeholders to scope analytical questions and translate business problems into data solutions.
• Design and maintain BI dashboards (Power BI, Omni) for self-serve reporting across teams.
• Conduct exploratory data analysis to surface trends, anomalies, and root causes in business performance.
• Present findings and recommendations clearly to both technical and non-technical audiences.

What Sets You Apart

• Strong hands-on coding ability in SQL and Python; comfortable building datasets and models from raw data, not just querying finished tables.
• Working knowledge of statistical and machine learning methods (regression, classification, clustering, time series forecasting) and when to apply them.
• Experience building and maintaining BI dashboards (Power BI, Looker, Tableau, or similar) for multiple business audiences.
• Ability to translate ambiguous business questions from Finance, Ops, Marketing, or Development into a defined anal