Senior Data Scientist

What You'll Do ::

Data Science & Modeling

• Develop and validate supervised learning models, including customer health scoring, churn prediction, upsell/cross-sell propensity, and lead scoring using Snowflake's ML platform capabilities.
• Perform feature engineering within the Snowflake Feature Store, building reusable feature sets that serve multiple models.
• Conduct exploratory data analysis and statistical validation to support modeling decisions and evaluate data readiness.
• Write clean, maintainable Python code and contribute to shared libraries and internal tooling.
• Work within our Snowflake data platform, writing SQL to explore, extract, and prepare data for modeling.

Deployment & Engineering

• Support the operationalization of models from development through production deployment within the Snowflake stack
• Contribute to model versioning and reproducibility practices as the team establishes standards
• Collaborate with the Enterprise Data & Intelligence team to ensure ML work integrates cleanly with the broader data platform, including dbt and Fivetran.

Communication & Collaboration

• Translate modeling results and analytical findings into clear, accessible presentations for technical and non-technical audiences.
• Partner with senior data scientists and the director to support end-to-end analytical workstreams.
• Contribute to team documentation, knowledge sharing, and internal standards as the function grows.
• Support the team's engagement with Deltek's AI Center of Excellence on model governance and integration opportunities.

Qualifications :

Required

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or a related quantitative field
• Solid Python skills, including hands-on experience with core ML libraries (scikit-learn, pandas, numpy)
• Understanding of machine learning concepts: supervised learning, model evaluation, feature engineering, training vs. scoring
• Foundational statistical fluency: hypothesis testing, regression, correlation, confidence intervals
• SQL proficiency sufficient for data exploration, feature extraction, and pipeline development
• Experience with Snowflake or other cloud data platforms; Snowpark ML or Snowflake Feature Store experience is a strong plus
• Familiarity with software engineering fundamentals including version control (Git) and modular code design
• Strong communication skills and ability to work collaboratively across technical and non-technical teams
• Comfort operating in a greenfield environment where practices are still being established.

Preferred

• Exposure to model deployment, experiment tracking, or pipeline orchestration in any environment
• Familiarity with ML Engineering or MLOps concepts such as versioning, reproducibility, or model lifecycle management
• Experience with SaaS business models and customer analytics use cases
• Familiarity with Power BI
• Exposure to CI/CD concepts and pipeline automation in a data or ML context