Data Scientist

AmeriLife•Washington, DC

1d•$125,000 - $135,000•Remote

About The Position

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.

Requirements

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
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)
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
Hands-on prompt engineering and LLM integration for production use cases
Experience building or orchestrating AI agents and multi-step workflows (LangChain, LangGraph, or similar)
Ability to evaluate AI outputs systematically and implement guardrails for reliability and safety
Strong judgment on when to apply traditional ML vs. generative AI and the trade-offs involved
3+ years in data science, machine learning, or applied AI roles
Proven ability to communicate complex results as clear, actionable insights for technical and non-technical audiences
Full production ownership experience — from problem definition through deployment, monitoring, and iteration
Bachelor’s or Master’s in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, or related quantitative field. Equivalent experience with a strong portfolio also considered.

Nice To Haves

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
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
Azure ecosystem experience (Data Factory, Azure AI Services, Azure OpenAI); MLOps practices and model monitoring
Certification: Azure Fundamentals or AWS Cloud Practicioner
Enterprise LLM experience (fine-tuning, evaluation, deployment); RAG architectures with vector databases and semantic search
NLP/text analytics; transformer architectures; Databricks Mosaic AI or Foundation Model APIs
Structured AI evaluation methods (offline/online testing, human-in-the-loop); computer vision exposure
Insurance, financial services, healthcare, or regulated industry experience
Track record of influencing technical direction and elevating team practices

Responsibilities

Partner with engineering, analytics, and business stakeholders to translate complex problems into scalable, production-ready solutions.
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
Evaluate and iterate on both traditional ML and generative AI solutions to ensure they deliver reliable business value.