Data Scientist​/Engineer

Shift is the leading AI platform for insurance. Shift combines generative, agentic, and predictive AI to transform underwriting, claims, and fraud and risk - driving operational efficiency, exceptional customer experiences and measurable business impact. Trusted by the world's leading insurers, Shift delivers AI when and where it matters most, at scale and with proven results.

Our culture is built on innovation, trust, and a drive to transform the insurance industry through our SaaS platform. We come from more than 50 different countries and cultures and together we are creating the future of insurance.

• This role is part of our Data Science team which is the largest team in our organization consisting of over 200+ Data Scientists throughout the world.
• Our Data Scientists work in a full lifecycle role and on a broad range of subjects acquiring extensive technical and professional experience in data science, data engineering, coding, business understanding and client engagement.
• Our company is small enough that each person’s achievements has an impact on our overall performance, yet big enough to be a world leader and innovator in our domain.
• As a member of the data science team, you will be working alongside our technical experts and your role will be key to rolling out our enterprise level solutions to our clients.

• Your role will be to actively contribute to the US
  - Insurance roadmap and clients, and working on various data types such as structured data, free text, documents and images.
• Build and product ionize data pipelines (structured, text, documents, images) optimized for LLMs and multi-modal models.
• Design, develop and deploy LLM-based solutions (RAG, embeddings, instruction tuning) for subrogation, claims handling, document understanding, and related use cases.
• Experiment with the latest in Agentic AI technologies (Langchain/Langgraph, OpenAI Agent SDK, MCP, A2A) and develop MVP for the next generation of autonomous subrogation solutions.
• Develop Chain-of-Thought and Re Act prompting strategies to ensure the agent can justify its liability percentages based on the Comparative Negligence laws of different jurisdictions.
• Create custom tools for the agent, allowing it to query internal databases, call external weather APIs, or calculate impact force based on telemetry data.
• Establish rigorous evaluation frameworks (LLM-as-a-judge) to ensure the agent’s decisions are unbiased, legally sound, and explainable.
• Ensure responsible-AI practices: privacy, hallucination mitigation, explainability and compliance.
• Lead client workshops, present prototypes
  , gather feedback and help define roadmap priorities.

We are looking for candidates with diverse skills to help us build excellent technology solutions for our clients and be proficient in the following skills:

• Expert proficiency in production-level object-oriented programming (OOP) for building scalable and reliable systems.
• Proven hands‑on experience with Large Language Models (LLMs) and generative AI techniques (including RAG, embeddings, prompt engineering, and model tuning
  ), leveraging frameworks such as OpenAI/Anthropic or open-source variants.
• Solid foundation in ML fundamentals with practical experience in the full machine learning lifecycle, including model evaluation, monitoring, versioning, and deployment in production environments.
• Experience designing and implementing robust data pipelines for document, OCR, and multi‑modal data workflows
  .
• Agentic Frameworks: Experience with integrating frameworks like Lang Chain/Lang Graph, OpenAI Agent SDK, CrewAI, A2A/MCP with Databricks or Azure‑hosted models (e.g., DBRX, OpenAI GPT‑5.3, Anthropic Claude, Google Gemini).
• Demonstrated ability to effectively engage with clients, translate complex business needs into clear, actionable technical solutions, and manage stakeholder expectations.

• Databricks Ecosystem: Deep expertise in Mosaic AI (formerly Mosaic
  
  ML),
  Unity Catalog
  , and Delta Lake
  . Good understanding of Spark data architecture is a plus.
• Experience using MLflow for the full lifecycle: from experiment tracking and prompt engineering…