Data Scientist, Agentic AI

Intersources Inc.
Boston, MA, United States
29 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
3 years minimum
Working hours
Regular working hours

Tech stack

LangGraph Framework Artificial Intelligence Data Analysis Computer Programming Continuous Integration Information Engineering Distributed Systems Github Python (Programming Language) Machine Learning Performance Tuning Tensorflow
+17 more
Software Engineering Unstructured Data Reinforcement Learning Data Logging Data Processing Pytorch LangChain Retrieval-Augmented Generation Large Language Models Random Forest Agentic-AI Information Technology HuggingFace Xgboost Machine Learning Operations AIOps Software Version Control

Job description

  • Lead the design and implementation of agentic AI solutions and LLM-powered applications that automate business processes and improve customer and employee experiences.
  • Drive the end-to-end model lifecycle: data exploration and preparation, model finetuning, validation, deployment, and monitoring, ensuring quality, security, scalability, and fairness.
  • Apply LLMs and generative AI to process and interpret unstructured data (e.g., insurance applications, underwriter’s notes, medical records, customer interactions).
  • Develop autonomous agents, multi-step reasoning systems, and advanced NLP pipelines that integrate with Guardian’s platforms.
  • Translate research in agentic AI, LLMs, and reinforcement learning into practical, production-ready solutions for underwriting automation, claims automation, customer servicing, and risk assessment.
  • Collaborate with data engineers, MLOps/AIOps, and product teams to ensure robust, scalable, and maintainable solutions.
  • Contribute to the standardization of tools, processes, and best practices.
  • Adhere to AI/LLM governance, documentation, testing, and other best practices in partnership with key stakeholders.
  • Present results and recommendations to technical and non-technical audiences, including senior leadership.
  • Stay current on industry trends, emerging technologies, and best practices in agentic AI, LLMs, and NLP.

Requirements

We are seeking a Senior Data Scientist, Agentic AI-an experienced individual contributor with deep expertise in Agentic AI, large language models (LLMs) with a track record of delivering enterprise-level AI/ML solutions and products, and natural language processing (NLP). In this role, you will be working in a team led by a Lead Data Scientist to develop and deploy advanced agentic AI and LLM-powered solutions that automate business workflows, enhance decision-making, and deliver measurable business outcomes. You’ll collaborate with cross-functional teams of other Data Science, Data Engineering, and Business groups. Successful candidates have a strong technical foundation, excel in coding, are detail-oriented, and have a passion for analytical thinking and problem-solving., * Passionate about applying advanced AI techniques-especially agentic AI, LLMs, and NLP-to solve real-world business challenges.

  • Analytical, intellectually curious, and experienced in leading the development and implementation of data and analytic solutions.
  • A hands-on builder who enjoys moving solutions from prototype to deployment and seeing them deliver value in production.
  • Comfortable collaborating in cross-functional teams and aligning technical solutions with business goals.
  • An effective communicator, able to present complex ideas and trade-offs to both technical and non-technical stakeholders.

You have:

  • PhD with 2+ years, Master’s with 4+ years of experience in Computer Science, Engineering, Statistics, Applied Mathematics, or related field.
  • Experience in Insurance Underwriting
  • 3+ years of hands-on experience in AI/ML modeling and development, with a primary focus on agentic AI, LLMs, and NLP.
  • Background in insurance and underwriting preferred
  • Deep expertise in LLMs, generative AI, agentic systems, NLP, and multi-step reasoning architectures (e.g., LangGraph, RAG, agent frameworks).
  • Strong programming skills in Python and familiarity with frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, and LangChain.
  • Demonstrated experience building, fine-tuning, and deploying LLMs and NLP solutions in production environments.
  • Working knowledge of classical machine learning techniques (e.g., clustering, regression, XGBoost, Random Forest, decision trees) and their practical applications.
  • Experience with data wrangling, distributed computing, and applying parallelism to ML solutions.
  • Proven experience in providing technical leadership and mentoring to data scientists and strong project management skills with the ability to monitor/track performance for enterprise success
  • Working knowledge of core software engineering concepts (version control with Git/GitHub, testing, logging, CI/CD).
  • Excellent analytical, problem-solving, and communication skills.
  • Experience in insurance, financial services, or related industries is a plus.

About the company

Client is on a transformation journey to evolve into a modern, forward-thinking insurance company committed to enhancing the well-being of its customers and their families. This role presents a distinctive opportunity to drive real-world impact by applying cutting-edge AI to transform how Client does business.

Client’s Data & AI team spearheads a culture of intelligence and automation across the enterprise, creating business value from advanced data and AI solutions. Our team includes data scientists, engineers, analysts, and product leaders working together to deliver AI-driven products that power growth, improve risk management, and elevate customer experience.

Client created the Data Science Lab (DSL) to reimagine insurance in light of emerging technology, evolving consumer needs, and rapid advances in AI. The DSL expedites Guardian’s transition to data-driven decision making and fosters innovation by rapidly testing, scaling, and operationalizing state-of-the-art AI.

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