Senior Applied Analytics Engineer

The Hartford•Columbus, OH

21h

About The Position

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future. Overview of Role The Sr. Applied Analytics Engineer drives the design, engineering, and delivery of AI-powered analytics solutions within The Hartford's Applied Analytics function. This role sits at the intersection of Artificial Intelligence, Business Intelligence, and production-grade engineering — building and operationalizing conversational AI agents and agentic analytics workflows. Responsible for designing sophisticated AI solutions that enable business users to effortlessly explore complex insurance data and derive actionable insights in plain English, while also owning the engineering lifecycle that makes those solutions reliable, scalable, and maintainable in production. This position is for a builder who is passionate about transforming data exploration, democratizing access to insights, and delivering sustainable, production-ready AI capabilities within a dynamic enterprise environment. Our Core Values We build AI solutions, not models. We are thoughtful in supporting the end-to-end business problem, with an eye to systems design. We are trusted and transparent. We collaborate tightly with our partners and are mindful of their capacity to absorb change. We provide assets that are safe to buy. Our products are delivered with a full monitoring solution to ensure our products continue to deliver as expected. We will earn the right to influence. With humble confidence, we listen carefully to learn from our customers and become partners in problem solving. We are practical and evolutional. We first deliver a minimally viable product and over time expand its sophistication based on feedback.

Requirements

Business Intelligence & Data Literacy: Strong understanding of BI principles, tools, and data warehouse/data lake environments. Ability to work with and interpret complex insurance data structures to support AI-driven analytics.
Conversational AI System Development: Experience in designing, developing, and deploying conversational AI systems, including RAG pipelines, chatbots, virtual assistants, and intelligent agents.
Python & Data Proficiency: Proficiency with pandas and numpy. Strong SQL skills for complex data exploration, feature engineering, and knowledge preparation.
End-to-End Modeling & Deployment Lifecycle: Experience with requirements gathering, experiment design, offline evaluation, production deployment, and post-production monitoring/validation of AI and BI solutions.
MLOps & CI/CD: Experience building and maintaining CI/CD pipelines (Jenkins, GitHub Actions, or equivalent) for ML model deployment. Familiarity with experiment tracking, model registries, and evaluation gates.
Cloud Platforms & Infrastructure: Development experience using the AWS and GCP suites of tools. Familiarity with SageMaker, Vertex AI, Bedrock, or Azure AI Services. Experience with Infrastructure as Code (CloudFormation, Terraform, or similar).
Data Pipeline Architecture: Experience with solution design and architecture of data pipelines, including workflow automation platforms (Apache Airflow, Autosys, or similar) and big data technologies (Spark, Hive, Snowflake, BigQuery, or Redshift).
Core Algorithms & Architectures: Solid understanding of natural language processing methods and generative AI architectures.
NLP & Generative AI Fundamentals: In-depth knowledge of embeddings, hybrid+dense retrieval, advanced chunking strategies, prompt engineering, structured outputs, and the fundamentals of agent/tool-use for conversational AI.
Version Control & Engineering Practices: Expert-level Git experience including GitHub Actions. Proficient with Unix; experience with reproducible notebooks/pipelines. Strong object-oriented development skills in Python. Container/cloud literacy (Docker, Kubernetes).
Communication: Exceptional ability to explain complex design trade-offs, evaluation results, and risks to both highly technical and non-technical business audiences, translating directly into clear business outcomes and strategies.
Insurance Domain Knowledge: Foundational understanding of insurance products and processes, with emphasis on operations, service, and workflow optimization. Familiarity with underwriting, claims, pricing, and relevant regulatory environments.
5 to 9 years of experience with Bachelor's degree.
Less than 5 years accepted with Master's or Ph.D.
4+ years of Python and SQL development experience.
1+ years of experience in the insurance or broader financial services industry preferred.
Candidate must be authorized to work in the US without company sponsorship.
The company will not support the STEM OPT I-983 Training Plan endorsement for this position.

Nice To Haves

RAG Expertise: Hands-on with vector databases and search (e.g., Vertex AI RAG Engine, OpenSearch, pgvector/Postgres), ANN indexing (HNSW), advanced rerankers (cross-encoders), and evaluation frameworks (RAGAS, TruLens, DeepEval) tailored for conversational AI in insurance.
Document AI Tooling: Practical experience with PyMuPDF/pdfplumber, Apache Tika; advanced OCR (Tesseract); layout-aware models (LayoutLM); and table extraction (Camelot/Tabula) for processing insurance documents.
Orchestration Frameworks: Familiarity with LangChain, LangGraph, or LlamaIndex; structured tool/function calling and guardrails for complex AI agents.
Embedding Model Selection: Experience comparing and selecting embedding models (OpenAI/Cohere/Voyage vs. open-source like bge/e5/gte) for domain-specific insurance corpora; understanding dimension/quality/cost/latency trade-offs.
Responsible AI & Safety: Expertise in bias/fairness testing, hallucination mitigation, grounding checks, safety filters; and comprehensive model risk documentation for conversational AI.
GenAI for Filing & Compliance: Exposure to GenAI applications in regulatory contexts, including document generation, objection response automation, and compliance-aware prompt engineering relevant to insurance.
Knowledge Graphs & RAG: Familiarity with building and querying domain-specific knowledge graphs and integrating them into RAG pipelines for enhanced retrieval and grounding.
Synthetic Data Techniques: Hands-on experience generating synthetic data using GenAI, including validation strategies and augmentation for low-frequency events in insurance.
Broader Modalities (Nice to Have): Experience with speech/vision/multimodal AI applications within an insurance context.
LLM Fine-tuning: Experience fine-tuning LLMs using PEFT/LoRA, and practical experience with model distillation.
Agile Experience: Experience working in an Agile framework with iterative delivery and continuous feedback loops.
Preference for Master's or Ph.D. in Machine Learning, Applied Mathematics, Data Science, Computer Science, or a similar analytical field, or progress towards a relevant professional designation.

Responsibilities

Design & Deliver Conversational AI Solutions: Build advanced generative and agentic AI solutions with a strong emphasis on conversational interfaces. This includes RAG pipelines, intelligent chat/assistant systems, and autonomous analytics workflows — leveraging a fit-for-purpose toolkit from structured retrieval to sophisticated agentic workflows.
Business Intelligence & Data Integration: Partner with data engineering and BI teams to ensure AI solutions consume and surface accurate, consistent, and interpretable insurance data — creating a unified and business-friendly view for AI agents and business intelligence tools.
End-to-End Solution Engineering & MLOps: Own the AI solution lifecycle from problem framing through production deployment and ongoing operations. This encompasses data preparation, model development, rigorous evaluation, CI/CD pipeline development, orchestration, observability, safety filters/guardrails, and comprehensive rollback plans. Design, develop, and maintain Models as Service, adopting and promoting MLOps best practices across the analytics engineering team.
Cloud Infrastructure & Deployment: Deliver production AI/ML solutions across AWS and GCP cloud environments. Develop repeatable architectural patterns using Infrastructure as Code (CloudFormation, Terraform, or similar), containerization (Docker, Kubernetes), and cloud-native ML platforms to ensure scalable, secure, and reproducible deployments.
Regulatory Intelligence & Filing Automation: Design and deploy GenAI capabilities to automate regulatory filing support for the insurance industry, including DOI objection response generation and the ingestion of legacy filings into searchable knowledge bases. Partner closely with Legal and Compliance to ensure all outputs meet evolving standards and enable direct API integrations with regulatory bodies.
Knowledge Base Engineering for Strategic Domains: Engineer and maintain robust, domain-specific knowledge bases (e.g., regulatory intelligence, competitive insights, customer sentiment specific to insurance) to power generative applications across operations, underwriting, pricing, and service. Structure knowledge for optimal retrieval by conversational AI systems.
Domain & Compliance Integration: Develop a deep understanding of The Hartford's business structures, processes, and data sources within the insurance context. Embed domain taxonomies, regulatory constraints, access controls, and security directly into solution design. Ensure adherence to responsible AI practices such as fairness, bias mitigation, transparency, and observability with compliance-by-design.
Stakeholder Collaboration: Partner with leaders and Subject Matter Experts (SMEs) across Operations, Business Insurance, Underwriting, Service, Claims, and Risk to align AI initiatives with core business goals. Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations. Define success criteria that balance accuracy, reusability, cost, and performance, translating complex model behavior into actionable strategies with clear ROI.
Unstructured Data & Retrieval Design: Prepare multi-format content (PDF, Office, HTML, images, audio) relevant to insurance with normalization, robust metadata/lineage management, and PII detection/redaction. Design advanced retrieval strategies (e.g., chunking, embeddings, hybrid search) tailored to insurance domain knowledge, and tune for cost, latency, and domain fit.
Prompt & Agent Design: Author robust system prompts, few-shot patterns, and structured outputs (e.g., JSON schemas) for conversational AI agents. Define safe tool-use policies and function/structured calling for reliable and ethical agent behavior within the insurance context.
Evaluation & Monitoring: Define comprehensive metrics across use cases for information retrieval, RAG/chat performance, and critical customer/operational KPIs. Build gold/synthetic test sets, support A/B testing, and monitor for drift, providing economic, qualitative, and statistical analysis to support thresholds and business decisions.
Mentorship & Technical Leadership: Work with junior engineers and peers to provide mentorship and thought leadership. Be comfortable presenting new concepts to technical audiences and championing best practices across the team.
Innovation & Continuous Learning: Research, experiment with, and implement suitable generative AI and agentic tools and technologies. Identify and pilot emerging methods (e.g., OCR for insurance documents, advanced rerankers, PEFT/LoRA, distillation). Build reusable accelerators (e.g., chunking templates, prompt registries, evaluation harnesses). Participate in identifying and assessing opportunities to ensure ongoing competitive advantage.