Data Scientist

• Lead and develop high-performing teams of data scientists, machine learning engineers, researchers, and technical contributors.
• Define and execute data science strategies that advance scientific discovery, research intelligence, and knowledge-access products.
• Drive the development of AI-powered capabilities across search, retrieval, recommendation, NLP, knowledge systems, and generative AI.
• Translate complex customer, scientific, and business challenges into scalable data science solutions and measurable outcomes.
• Establish high standards for experimentation, evaluation, model quality, reliability, and responsible AI practices.
• Partner closely with Product, Engineering, Research, UX, Analytics, and domain experts to shape product strategy and delivery.
• Mentor and coach team members while fostering a culture of scientific rigor, collaboration, innovation, and continuous learning.
• Guide the adoption of emerging AI technologies, including LLMs, retrieval-augmented generation, semantic search, and knowledge-based systems.
• Influence senior stakeholders and contribute to long-term AI, technology, and product strategy across the organization.
• Ensure that AI systems are trustworthy, scalable, explainable, measurable, and aligned with meaningful customer and societal outcomes.

Data Science Manager

• Perform model validations models on AI and GenAI model use cases across The Hartford’s functional areas and lines of business to ensure models are performing effectively and efficiently
• Ensure model calculations, machine learning algorithms, and GenAI methods are accurate and appropriate for intended use
• Design and build challenger solutions and/or testing methods for tasks such as summarization, question answering, search, data synthesis, llm-as-a-judge etc.
• Review and assess the quantitative and qualitative testing techniques to ensure model accuracy, robustness, and reliability
• Assess key data inputs, assumptions, prompt engineering, context engineering for accuracy and appropriateness
• Review model outputs for accuracy and appropriate downstream usage
• Deliver effective challenge to key modeling elements such as inputs, calculations, outputs, conceptual soundness, monitoring & controls, documentation, etc.
• Identify findings and recommendations, including impact analysis, to mitigate model risk and compile clear and concise model validation reports
• Perform governance accountabilities related to findings tracking, remediation testing, and validation
• Assist in enhancing existing GenAI model validation framework to include standardization evaluation metrics for performance and reliability, deployment of model validation tools for increased efficiency, and ensure continued alignment with regulatory standards
• Strengthen partnerships with Data Science teams to keep model risk practices aligned with the proliferation and sophistication of modeling, promote proactive risk management, and share best practices.
• Pro-actively stay informed with advancements in AI/ML, GenAI, and regulatory expectations for emerging technologies and of department initiatives, deliverables, and reporting
• Assist with the understanding and testing of cutting-edge tools, such as VertexAI/Google agent development kit, LangChain/LangGraph, RAG frameworks, HuggingFace, OpenAI APIs, etc.
• Assist in improving The Hartford’s Model Risk Management function in relation to AI and GenAI, by monitoring external environment, implementing emerging best practices, recommending process improvements, and evolving standards/guidelines.

Actuarial and Data Science Model Validation – AI, Gen AI

• Define Ads ontologies and metrics for campaigns, budgets, bids, creatives, audiences, and placements.
• Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs.
• Ingest and enrich unstructured Ads content and publish retrieval‑ready datasets using our managed search/vector services.
• Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and re‑ranking; set quality/latency targets and iterate via experiments.
• Design agent reasoning and policies on ads, including tool definitions and human‑in‑the‑loop approvals.
• Establish evaluation suites covering precision/recall, calibration, hallucination rate, latency, and cost.
• Run A/B or uplift experiments to quantify impact and guide iteration.
• Translate Ads problems into agent behaviors and own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time‑to‑insight.

Ads AI Analytics Lead

• Provide advanced analytics expertise within the Business Analytics function
• Independently apply data science techniques to solve business problems
• Develop, validate, and maintain predictive models and AI solutions
• Utilize multiple sources of data and various machine learning techniques
• Write clean, modular, and well-documented code for production
• Interpret data and model outputs to generate actionable insights

Senior Data Scientist – Level I

• Accountable for identifying, shaping and delivering strategic results through multiple business analytics disciplines.
• Works in collaboration with business leaders to provide thought partnership and drive decision making based on data and analytical insights across different parts of the organization.
• Consistently has a seat at the table with the leaders they support to influence the strategic direction of the organization and solve the most complex, highest priority problems.
• Able to make analytically sound decisions, often based on limited information, that inform solutions for business priorities.
• Reporting to the head of the Provider Operational Health team, responsible for providing strategy and analytics support for the operation.
• Lead multiple Strategy and Analytics teams supporting the PX&O organization, working closely with Operations partners to deeply understand the business problems at hand.
• Guide the development of analytical models/insights and build compelling business cases that positively influence outcomes.
• Develop business cases, secure funding for prioritized strategic initiatives and coordinate strategic efforts with broader enterprise business partners.
• Build and maintain strong partnerships with cross functional organizations within operations, tech, product, and business partner teams.

Business Analytics Lead

• Develop, test, validate, and maintain a portfolio of rating models for the Employee Benefits class plans in Long-Term Disability, Short-Term Disability, and Life
• Continuously partner with Actuarial and Data teams to monitor and manage the End-to-End lifecycle of the rating models and underlying data which feeds them
• Lead cross-functional projects that include the creation of statistical models and machine learning techniques to achieve financial objectives, solve business problems, and identify long-term opportunities that enhance actuarial modeling.
• Collaborate and partner with business stakeholders in a way that supports the vision and sustains a culture that treats analytics as a corporate asset.
• Advance the department’s capabilities by creating and deploying long-term tools to continually evolve the practice of data science, with an ability to see the end-to-end solution.
• Develop strategies to achieve targeted business objectives.
• Implement these strategies and follow through to successful conclusion.
• Remain current on research techniques and become familiar with state-of-the-art tools applicable to your function.
• Participate in the talent management process for hiring, onboarding, training and development of staff.
• Collaborate with your leader to provide timely feedback on development and opportunities for your team.
• Learn/bring best practices to guide the direction of our Data Science and Data Engineering workflows.

Director of Data Science, Actuarial Modeling

• Take full ownership of the data science and ML stack from the ground up, including tooling and infrastructure decisions.
• Proactively analyze available data to identify business opportunities and recommend ML/AI strategies accordingly.
• Drive the complete ML lifecycle: problem definition, data exploration, feature engineering, model development, deployment, and monitoring.
• Deliver hands-on ML solutions across various domains (e.g., forecasting, churn prediction, optimization).
• Communicate actionable insights and recommendations to both technical and non-technical stakeholders.
• Partner with leadership and cross-functional teams to prioritize high-impact data initiatives.
• Stay current with advancements in ML/AI and continuously bring innovation into our workflows.

Senior Data Scientist

• Serve as the primary point of contact for At Work-related data and analytics needs
• Partner with CAPTRUST’s centralized data team to prioritize, design, and improve data solutions supporting At Work
• Help establish consistent definitions, metrics, and reporting standards across the At Work practice
• Improve data quality, clarity, and usability over time
• Support and enhance internal and client-facing reporting used by At Work teams
• Work closely with Operations to understand recurring reporting needs and reduce one-off or manual efforts
• Develop dashboards and reports that support operational planning, advisor effectiveness, and leadership decision-making
• Translate reporting outputs into insights that inform action, not just describe activity
• Develop qualitative and quantitative measures of client and participant engagement, trust, and relationship health
• Help establish ongoing feedback loops across the client and participant lifecycle, beyond episodic surveys
• Identify trends that signal strengthening relationships, stagnation, or potential breakdowns
• Support analysis related to the annual financial wellness survey and other experience-related initiatives
• Analyze patterns associated with strong long-term outcomes such as sustained engagement, retention, and expansion of advisory relationships as needs evolve
• Partner with leadership and Operations to identify where additional investment of time or resources may be needed
• Help reduce reliance on anecdotal feedback through clearer, shared insight

Data & Analytics Lead

• Develop and deploy advanced statistical, machine learning, and AI models supporting insurance, actuarial, claims, and investment decision-making.
• Design and deliver LLM-enabled analytics and Deep Research solutions, including RAG-based approaches over structured and unstructured data.
• Perform hands-on data analysis, feature engineering, and model development using enterprise platforms (Snowflake, PostgreSQL, DB2).
• Define model success metrics, validation approaches, and explainability standards aligned to business and regulatory expectations.
• Partner with data engineers and architects to produce models, establish monitoring, and manage model lifecycle and drift.
• Apply responsible AI practices including bias assessment, transparency, and documentation for audit and compliance review.
• Communicate analytical insights and model outcomes clearly to business leaders and technical stakeholders.
• Collaborate across analytics, engineering, risk, and compliance teams to ensure scalable, governed AI and analytics delivery.

Director of Applied AI, Data Science

• Support end-to-end delivery of AI solutions: participate in problem framing, exploratory analysis, model development, and measurement of outcomes
• Develop and validate machine learning models under guidance (e.g., classification/regression, time series, anomaly detection, NLP as appropriate)
• Contribute to GenAI solutions by building and improving Retrieval-Augmented Generation (RAG) components (document preparation, chunking, metadata, and retrieval testing) and prompt templates following established patterns
• Perform data analysis and feature engineering: data quality checks, joins, transformations, leakage checks, and creation of reusable features
• Define and track model/LLM quality metrics with support (e.g., accuracy, calibration, stability; groundedness/factuality for LLM outputs), and help maintain evaluation datasets and test cases
• Assist with explainability and documentation: summarize model behavior, limitations, and key drivers; contribute to model cards and audit-ready artifacts
• Work with data engineers and platform teams to operationalize solutions: support pipeline integration, scheduling, monitoring, and troubleshooting
• Apply responsible AI practices: follow privacy/security requirements, handle sensitive data appropriately, and participate in bias/fairness reviews
• Communicate findings clearly: prepare concise readouts, visuals, and recommendations for technical and non-technical audiences

Applied AI, Data Scientist

• Setting the technical direction and measurement strategy for the shopping funnel
• Developing and mentoring data scientists across experimentation, causal inference, and product analytics
• Defining scalable frameworks for A/B testing, funnel optimization, and consumer behavior modeling
• Influencing senior product and business stakeholders with data-driven recommendations
• Ensuring analytical rigor translates into tangible product improvements and business growth
• Driving deep-dive analyses of renter behavior, market dynamics, and funnel performance to identify high-impact growth opportunities
• Managing and developing a team of data scientists working across shopping funnel measurement and consumer analytics
• Raising the bar on production-grade analytics and experimentation
• Operating as a strategic thought partner to senior stakeholders across Product, Engineering, and Business Strategy
• Leading thoughtful adoption of AI within your team.

Manager, Data Science

• Lead the design and delivery of advanced medical cost forecasting solutions that improve claim reserving and enterprise financial forecasting
• Prototype and validate new analytical approaches using techniques such as time series modeling, simulation, and machine learning
• Create diagnostics and performance measures to evaluate forecast accuracy and explain key cost drivers
• Partner with finance, actuarial, and business teams to translate forecasting needs into actionable analytics solutions
• Communicate findings through clear insights, presentations, and technical documentation to support decision-making
• Develop early-warning analytics to identify emerging cost trends and risks
• Collaborate with data engineering and product teams to ensure scalable and interpretable solutions
• Contribute to model monitoring, governance, and continuous improvement processes
• Mentor junior team members and share best practices across the data science community

Senior Advisor, Data Science – Medical Cost Trend & Forecasting

• Work From Anywhere as a Senior Revenue Cycle Business Analyst.
• Leverage complex data to guide high-level business decisions.
• Focus on data science to tackle operational challenges such as denials management, AR optimization, and cash forecasting.
• Develop, test, and deploy reports and datasets for accountability among business leaders.

Senior Revenue Cycle Analytics, Data Science Expert

• Develop AI solutions: Create ML and generative AI systems for RAG pipelines, chatbots, classification, forecasting, and recommendation.
• Ensure alignment with enterprise standards, seamless integration, and secure scalability.
• End ‑ to ‑ End Solution Development: Own the AI lifecycle from problem framing through deployment: data prep, modeling, evaluation, model change management, orchestration , observability , drift detection, and synthetic data generation.
• Collaborate closely with AI engineers, data engineers, platform, security, and IT to ensure solutions are robust, maintainable , production ready, follow safety filters/guardrails, and rollback plans.
• Drive execution from discovery to rollout by defining scope, milestones, and acceptance criteria; managing dependencies/risks; coordinating cross-functional workstreams; and maintaining clear status reporting, issue escalation, and delivery timelines.
• Partners closely with Product, Underwriting, Distribution, Risk, Legal, and Compliance to align AI initiatives with enterprise objectives and governance expectations.
• Translates complex model behavior and evaluation outcomes into clear, actionable business insights with defined success criteria (accuracy, cost, performance, reuse, ROI).

Senior Applied AI Data Scientist

• Build and optimize ML, deep learning, and GenAI models using modern frameworks (PyTorch, TensorFlow, scikit-learn)
• Design and deliver RAG pipelines, hybrid retrieval systems, and vector-based search workflows
• Develop Agentic AI solutions including tool orchestration, reasoning flows, and safe-execution strategies
• Run structured experimentation using evaluation metrics (BERTScore, BLEURT, semantic similarity, retrieval precision/recall)
• Integrate solutions into GCP (Vertex AI, Workbench, Vector Search) and AWS (SageMaker, Bedrock)
• Build ingestion, enrichment, and semantic retrieval flows for high-quality knowledge and feature engineering
• Partner with onshore and offshore DS/ML engineers to ensure quality, consistency, and shared technical patterns

Applied AI Data Scientist

• Lead Personal Insurance Actuarial Modeling: Serve as the model owner for personal insurance predictive models within actuarial, ensuring alignment with ratemaking and business priorities.
• Partner on strategic vision while managing team capacity and driving timely model deliverables.
• Model Development & Deployment: Lead design, maintenance, and deployment of predictive models for frequency, severity, and loss cost estimates, ensuring production readiness and effective integration into actuarial workflows.
• Cross-Functional Collaboration: Partner with Actuarial, Data Engineering, and other modeling organization teams to connect modeling initiatives with ratemaking strategies and organizational goals, promoting shared problem-solving.
• Innovation: Drive modernization through advanced modeling techniques, machine learning, and AI to enhance efficiency and decision-making.
• Lead experimentation with new data sources, features, and methodologies to uncover insights and advance strategic objectives.
• Operational Excellence: Ensure robust documentation, validation, and governance, along with scalable processes to monitor model performance and maintain reliability.
• Talent Development: Mentor and develop team members, fostering a culture of curiosity, ownership, and continuous learning.
• Support hiring and onboarding, including intern and actuarial student rotations.
• Strategic Influence: Participate in enterprise initiatives to ensure personal insurance predictive models are efficiently integrated into core data and system platforms.

Director of Data Science

• Create statistical models, algorithms, and machine learning techniques to enhance traditional actuarial processes and assumptions
• Think creatively to envision how we can enhance long-standing actuarial methodology using statistical modeling and machine learning techniques
• Use R and/or Python to build, maintain, and support loss models across different lines of business
• Participate in reviewing work with business partners and team members on an ongoing basis to calibrate deliverables against expectations and effectively translate results to business initiatives
• Develop knowledge of The Hartford's formal and informal structures, business processes, and data sources
• Participate in the creation and deployment of long-term tools to continually evolve the business
• Contribute to the successful implementation of strategies to achieve targeted business objectives
• Remain current on research techniques and become familiar with state-of-the-art tools applicable to your function

• Gather, preprocess, and validate datasets from internal and external sources.
• Identify trends, patterns, and anomalies using statistical and visual techniques.
• Build and evaluate predictive models using machine learning algorithms.
• Create dashboards and visual reports to communicate findings to stakeholders.
• Maintain clear records of methodologies, code, and project outcomes.
• Work closely with cross-functional teams to align data solutions with business needs.

Data Science, AI Intern

• Collaborate with leaders across the organization to develop and execute on a data strategy inline with ActBlue's near and long term goals, including supporting organizational growth through acquisitions and partnerships
• Foster a data centric culture by upleveling our ability to make data-informed decisions and build best in class data products that meet rigorous performance and reliability standards
• Enable and advocate for AI/ML driven products and features across the ActBlue Platform through building scalable machine learning infrastructure that serves production environments and identifying new opportunities to deploy data science and analytics capacity in service of our mission
• Oversee our Data Science, Data Analytics, Data Engineering, and Data Platform teams in a fast-paced, mission-driven environment
• Guide the evolution of our data systems, tools, and architecture to meet the organization's needs, including data consolidation, governance frameworks, and unified data models across multiple entities
• Ensure high-quality communication and a stakeholder-centric approach between our data teams and their internal customers, with an emphasis on responsiveness and partnership during high-stakes moments

Director of Data Science – Engineering

• Design, build, and deploy production-grade ML models with end-to-end pipelines and MLOps best practices to ensure scalability, reproducibility, and continuous improvement.
• Lead the design and deployment of GenAI solutions leveraging LLMs, RAG, prompt engineering, and fine-tuning to build intelligent assistants, conversational agents, and knowledge retrieval tools.
• Analyze large-scale datasets using statistical methods and advanced analytical frameworks to uncover actionable patterns and measure AI/ML impact.
• Partner cross-functionally to deliver high-impact AI/ML use cases—including predictive analytics, anomaly detection, and workflow automation—across enterprise digital workplace platforms.
• Serve as a subject matter expert in ML and GenAI, mentoring team members, driving actionable business recommendations, and contributing to responsible AI governance frameworks.

See all jobs on JobTailor