Data Scientist

Role Overview

Moultrie is hiring a Data Scientist for its Data & Analytics team. The team develops insights that support decision-making across the company by building predictive and prescriptive modeling capabilities on top of a foundational data layer, delivering actionable outputs through BI and downstream systems.

Responsibilities

• Predictive & Prescriptive Modeling
  • Design, build, and maintain predictive models for business questions such as customer churn, subscription health, and purchase propensity.
  • Deliver model outputs as attributes usable in downstream BI products (e.g., Tableau, Streamlit) and activation platforms (e.g., BlueConic, Braze, TripleWhale).
  • Partner with business stakeholders to scope ambiguous requests into modeling problems with success metrics.
  • Communicate model results and business implications to non-technical audiences.
  • Track, document, and monitor experiments and deployed models for reliability, understandability, and reproducibility.
• Feature Store & Data Engineering (Data Science in the Stack)
  • Build and maintain features in the feature store: define features, document refresh cadence, and ensure feature pipelines are reliable and tested.
  • Create and document training datasets (assumptions, evaluation windows, limitations).
  • Collaborate with data engineers to ensure data models support feature engineering and model training.
  • Apply data engineering fundamentals such as SQL modeling, version control, and documentation.

Requirements

• 4+ years of hands-on experience in data science or a role with significant applied modeling.
• Experience building and deploying predictive models in a business context.
• Strong Python skills, including use of libraries such as scikit-learn and pandas.
• Experience with classification and regression, plus validation using metrics such as precision, recall, and AUC.
• SQL proficiency for clean, maintainable code.

Nice to Have / Implied

• Familiarity with feature stores and model deployment workflows using Snowflake.
• Experience with experiment tracking and deployment monitoring.

Tech / Tooling Mentioned

• Snowflake (Model deployment, ML Functions, Snowpark)
• Python, SQL
• Snowflake feature store, dbt
• MLflow
• Tableau, Streamlit
• Activation platforms: BlueConic, Braze, TripleWhale