Data Scientist

Our Company

Explore how you can contribute at AmeriLife.

For over 50 years, AmeriLife has been a leader in the development, marketing and distribution of annuity, life and health insurance solutions for those planning for and living in retirement.

Associates get satisfaction from knowing they provide agents, marketers and carrier partners the support needed to succeed in a rapidly evolving industry.

Job Summary

AmeriLife is a national leader in insurance and financial services. Our AI & Data Science team is small, high-impact, and building a modern AI capability from the ground up on Databricks. You’ll ship production solutions — from predictive forecasting to AI agents — that directly transform how the business operates across our Health and Wealth verticals.

Job Description

Why This Role Stands Out

• Databricks-first platform — Lakehouse architecture with Unity Catalog, MLflow, and scalable compute
• Real AI work — design and deploy AI agents, RAG systems, and LLM-powered solutions in production
• End-to-end ownership — from problem framing through deployment and monitoring
• Direct business impact — work with business leaders on high-visibility initiatives
• Shape the future — influence technical direction, tooling, and best practices on a growing team

What You’ll Do

As a Data Scientist on our AI & Data Science team, you will partner with engineering, analytics, and business stakeholders to translate complex problems into scalable, production-ready solutions. Your work will span the full lifecycle — from exploratory analysis through model deployment and ongoing optimization.

Day-to-day, you will design and build predictive models for forecasting and optimization, develop intelligent automation using AI agents and large language models, engineer features and pipelines on the Databricks Lakehouse platform, and evaluate and iterate on both traditional ML and generative AI solutions to ensure they deliver reliable business value.

This role is ideal for someone who thrives at the intersection of rigorous statistical thinking, practical data engineering, and applied AI innovation — and who wants to do that work on a modern platform where their contributions are visible and valued.

Technical Requirements

Statistics & Machine Learning

Required

• Strong foundation in statistical modeling and ML, with experience selecting appropriate approaches for different business problems
• Proven experience building and validating time-series forecasting models in production environments
• Hands-on expertise with ensemble/boosting algorithms (XGBoost, LightGBM) for structured data
• Experience with A/B testing design, hypothesis testing, and rigorous model evaluation techniques
• Comfort working with imperfect, real-world datasets — handling missing data, class imbalance, and feature drift

Preferred

• Unsupervised learning (clustering, anomaly detection), hierarchical/probabilistic forecasting, Bayesian methods, or causal inference
• Experience optimizing models for business ROI; exposure to reinforcement learning or advanced optimization

Databricks Platform & Data Engineering

Required

• Strong Python proficiency for data analysis, modeling, and production development
• Experience with Databricks notebooks, clusters, and workflows for data science and ML
• Working knowledge of PySpark for large-scale data processing and feature engineering
• Advanced SQL skills across Lakehouse architectures; experience with MLflow for experiment tracking and model registry
• Clean, testable code practices with Git-based version control (e.g., Databricks Repos, GitHub)

Preferred

• Unity Catalog, Delta Lake/Delta Live Tables, medallion architecture patterns
• Databricks Feature Store, Model Serving, Workflows orchestration, or data quality frameworks
• Experience designing APIs for model serving; CI/CD for ML workflows
• Databricks Accreditations: Fundamentals 

Cloud & Infrastructure

Required

• Hands-on cloud platform experience (Azure preferred; AWS/GCP also valued) with Docker containerization
• Understanding of cloud-native data architectures, distributed processing, and data governance/RBAC

Preferred

• Azure ecosystem experience (Data Factory, Azure AI Services, Azure OpenAI); MLOps practices and model monitoring
• Certification: Azure Fundamentals or AWS Cloud Practicioner

Applied AI & Intelligent Automation