Data Scientist

Role Overview

As a Data Scientist on AmeriLife’s AI & Data Science team, you’ll build and ship production AI solutions on a Databricks-first platform. The work is high-impact and end-to-end, ranging from forecasting and optimization to AI agents and LLM-powered systems across the company’s Health and Wealth verticals.

What You’ll Do

• Partner with engineering, analytics, and business stakeholders to translate complex problems into scalable solutions.
• Own the full lifecycle: problem framing → exploratory analysis → model deployment → monitoring and optimization.
• Design and build predictive forecasting and optimization models.
• Develop intelligent automation using AI agents and LLMs.
• Engineer features and data pipelines on the Databricks Lakehouse.
• Evaluate and iterate on both traditional ML and generative AI solutions to ensure reliable business outcomes.

Technical Requirements

Statistics & Machine Learning

• Strong foundation in statistical modeling and ML.
• Proven experience building and validating time-series forecasting models in production.
• Hands-on experience with ensemble/boosting for structured data (e.g., XGBoost, LightGBM).
• Experience with A/B testing design, hypothesis testing, and rigorous evaluation.
• Ability to work with imperfect data (e.g., missing data, class imbalance, feature drift).

Databricks Platform & Data Engineering

• Strong Python proficiency for analysis and production development.
• Experience with Databricks notebooks, clusters, and workflows.
• Working knowledge of PySpark.
• Advanced SQL skills in Lakehouse architectures.
• Experience with MLflow for experiment tracking and model registry.
• Clean, testable code and Git-based version control (e.g., Databricks Repos/GitHub).

Preferred Qualifications

• Unity Catalog, Delta Lake/Delta Live Tables, and medallion architecture patterns.
• Databricks ecosystem: Feature Store, Model Serving, Workflows orchestration, and/or data quality frameworks.
• Experience designing APIs for model serving and CI/CD for ML workflows.
• Unsupervised learning, hierarchical/probabilistic forecasting, Bayesian methods, and/or causal inference.
• Experience optimizing models for business ROI; exposure to reinforcement learning or advanced optimization.

Cloud & Infrastructure

• Hands-on cloud experience, Azure preferred (AWS/GCP also valued).
• Experience with Docker and containerization.