Title: Generative Artificial Intelligence Senior Data Scientist / Generative Artificial Intelligence Data Scientist Location: - Hartford, CT - Columbus, OH - Charlotte, NC - Chicago, IL Remote Full time Job Description: Sr Data Scientist - GD07AE Data Scientist - GD08AE 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. Senior Data Scientist - AI Horizontal Products Step into the future with The Hartford as a Senior Data Scientist, where Generative AI is a core strategic capability-it's central to our enterprise strategy. Join a team that's pioneering AI‑driven products across underwriting, claims, and customer service, transforming how we operate and make decisions at scale. As part of our Horizontal AI Products Team, you will investigate emerging models, architectures, and techniques; design rigorous evaluation frameworks; and prototype new products that inform how GenAI is operationalized across the organization. This position is ideal for someone who is intellectually curious, technically deep, and motivated by advancing the state of practice in enterprise Generative AI. This position offers a Hybrid or Remote work arrangement. Candidates living near one of our locations are expected to work in the office three days per week (Tuesday-Thursday). Research & Technical Responsibilities - Conduct applied research in Generative AI, exploring emerging foundation models, architectures, and inference strategies to address enterprise AI use cases. - Lead investigation and experimentation across areas such as: - Agent‑based and multi‑step reasoning workflows - Retrieval‑augmented generation (RAG) - Long‑context modeling and information synthesis - Prompting strategies and controllability of generative outputs - Design and execute rigorous experimental frameworks to evaluate model quality, robustness, latency, cost, and reliability using: - Automated metrics - Human‑in‑the‑loop feedback - LLM‑as‑a‑judge and comparative evaluation methodologies - Create benchmarks and build reproducible pipelines to compare vendor models, open‑source alternatives, and internal approaches. - Rapidly prototype research ideas using modern GenAI tooling such as: - Vertex AI / Google Agent Development Kit - LangChain / LangGraph - RAG frameworks - Hugging Face ecosystems - OpenAI APIs - Translate research findings into deployable GenAI capabilities, working closely with engineering and MLOps teams to ensure scalability, observability, and responsible AI practices. - Contribute to the enterprise GenAI roadmap by identifying gaps, risks, and opportunities informed by ongoing research and industry trends. - Partner with stakeholders across Data Science, Technology, and Business teams to align research directions with real‑world operational challenges. Qualifications - Masters and 5+ years of industry experience OR PhD and 3+ years of industry experience in machine learning or data science and with 1+ years focused on GenAI. - Strong proficiency in Python and modern AI/ML frameworks - Demonstrated experience designing experiments, running evaluations, and drawing conclusions from empirical results. - Hands‑on experience with Generative AI frameworks and platforms, including Vertex AI, ADK, LangChain/LangGraph, RAG pipelines, Hugging Face, and OpenAI APIs. - Deep understanding of: - Agent workflows and tool‑augmented LLMs - Prompt engineering and model controllability - Retrieval‑augmented generation (RAG) design trade‑offs - Generative model evaluation and benchmarking techniques - Experience collaborating with engineering and MLOps teams to transition prototypes into production systems. - Strong written and verbal communication skills, including the ability to explain research findings, limitations, and trade‑offs to both technical and non‑technical audiences. - Authorization to work in the United States without company sponsorship. STEM OPT I‑983 Training Plan endorsement is not supported for this role. Compensation The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford's total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is: $90,160 - $166,080 The posted salary range reflects our ability to hire at different position titles and levels depending on background and experience. Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age About Us | Our Culture | What It's Like to Work Here | Perks & Benefits

• Focuses on bridging the gap between complex epidemiological and contextual data and strategic public health action. • Accompany in-country counterparts to generate stronger analytics and information products to support decision-making, integrating disease surveillance data with other contextual data. • Contribute to the facilitation of two flagship programs - Epidemic Intelligence training curriculum, and a co-design workshop - to build national counterparts' capacity for epidemic intelligence and systems thinking. • Collaborates with HQ-based staff and in-country embedded teams on data analytics, innovations and delivery. Responsibilities include: • Support the implementation of epidemic intelligence activities that inform decision-making, including development of innovative data use products and implementation of automated production pipelines. • Collaborate closely with in-country counterparts and across teams to map existing systems, data, and decision-making needs. • Provide hands-on technical accompaniment, capacity building, and knowledge transfer, including providing hands-on technical mentorship that develops local talent. • Training in-country embedded teams on the use of digital tools, such as DHIS2, Emergency Management Systems (EMS), R and R markdown for data management, analytics, GIS, and data product generation. • Work with a multidisciplinary team to develop a country workplan and budget for Collaborative Surveillance, coordinate the implementation, and generate strategic reports for leadership and donors. • Collaborate with global (e.g., WHO, US CDC, UNICEF) and country-level partners to streamline and align Collaborative Surveillance initiatives. • Provide technical oversight and methodologies to evaluate the impact of the Collaborative Surveillance initiatives. • Lead consultative work to investigate and develop use cases for surveillance innovations. • Collaborate with multidisciplinary epidemic intelligence team to pioneer innovations in data use for decision-making.

• Lead National Pricing & Quoting Strategy • Own national pricing programs for data center grey space projects across multiple regions • Develop and manage detailed quotes for large-scale electrical scopes • Build standardized pricing models, labor assumptions, and BOM structures • Partner with manufacturers to negotiate pricing, rebates, and supply agreements • Ensure competitive positioning while maintaining margin expectations • Act as a senior point of contact for national data center customers and contractors • Support pre-bid meetings, scope alignment, and high-level customer discussions • Guide internal teams on pricing strategy and technical requirements • Collaborate with field teams and operating companies to support project delivery • Provide technical guidance on material selection and substitutions • Troubleshoot supply chain, logistics, and coordination challenges • Drive Vendor Strategy & Standardization • Promote standardization of materials across projects and customers • Identify value engineering and prefabrication opportunities • Support long-term vendor relationships aligned with growth

• Stay up-to-date with best practices and technologies related to data acquisition and web scraping. • Generate data analyses based on captured data or data provided in databases. • Process captured data and make it available for consumption in development and production data stores. • Assist in maintaining and continuously improving data normalization and delivery pipelines. • Perform performance analyses regarding the data being captured and/or made available. • Develop Python scripts to perform web scraping of specific websites as requested by commercial or business teams. • Review the production queue to identify the next site to be mapped, create the corresponding script, test it in the development environment, and validate the collected data. • Collaborate with other areas of the company to understand data needs and ensure acquired datasets meet established requirements. • Validate acquired data to ensure quality and consistency, correcting any errors or inconsistencies identified. • Connect to AWS servers to perform data captures. • Drive continuous improvement of data acquisition processes to increase efficiency and data quality. • Receive and distribute acquisition requests among the team. • Create reports on the progress of requests handled by the acquisition team. • Assist in the development of the acquisition team. • Conduct training for members of the acquisition team.