Data Scientist - ML and Advanced Analytics

About

Our client is seeking a Data Scientist with deep expertise in machine learning, statistical modeling, and applied analytics. This is a hands‑on individual contributor role with opportunities to lead project workstreams and mentor junior team members within Insurance Technology. The ideal candidate has strong technical capabilities and can deliver end‑to‑end projects across various areas of the business. In this role, you will design, build, and deploy machine learning models or AI Agents to ensure delivery of intelligent automation solutions across high‑visibility initiatives.

Key Responsibilities

• Design, build, and deploy AI agents leveraging large language models (LLMs), tool orchestration, and decision logic to automate complex business workflows and analytical tasks.
• Develop agent architectures (single- and multi‑agent systems) that integrate models, APIs, databases, and enterprise systems to execute end‑to‑end automation reliably.
• Implement prompt engineering, retrieval‑augmented generation (RAG), memory, and reasoning strategies to improve agent accuracy, robustness, and contextual awareness.
• Evaluate, fine‑tune, and govern AI agent behavior using systematic testing, logging, monitoring, and human‑in‑the‑loop feedback mechanisms.
• Partner with engineering, product, and business teams to identify opportunities where autonomous or semi‑autonomous agents can reduce manual effort, improve speed, or enhance decision quality.
• Operationalize AI agents using MLOps/LLMOps best practices, including versioning, monitoring, cost management, security, and responsible AI controls.
• Assess and manage risks related to AI agents, including hallucination, bias, data privacy, security, and failure modes, and implement appropriate safeguards.
• Build reusable agent components, tools, and frameworks to accelerate development and promote standardization across teams.
• Measure and communicate the business impact of AI agents through defined success metrics such as efficiency gains, accuracy improvements, and user adoption.
• Stay current with advancements in agentic AI, LLM platforms, orchestration frameworks, and automation technologies, and translate emerging capabilities into practical enterprise solutions.

What You Bring

Mindset & Collaboration

• Passion for applying advanced analytics and machine learning to solve complex, real‑world business problems.
• Intellectual curiosity and a strong interest in exploring new AI/ML techniques and understanding when and how to apply them effectively.
• A hands‑on builder mentality, with experience taking solutions from early exploration through deployment and adoption.
• Comfort working in cross‑functional, multidisciplinary teams that include engineers, analysts, and business leaders.
• Strong communication skills, with the ability to explain complex concepts, assumptions, and trade‑offs to diverse audiences.
• Experience providing technical guidance and mentorship to other data scientists while remaining an active contributor.

Qualifications & Experience

• Advanced degree in Statistics, Computer Science, Engineering, Applied Mathematics, or a related field, with experience commensurate to degree level.
• 3+ years of hands‑on experience developing, validating, and deploying machine learning models in applied settings.
• Strong foundation in probability, statistics, experimental design, and statistical modeling.
• Proficiency in Python and experience with common data science and ML libraries (e.g., pandas, NumPy, scikit‑learn or similar).
• Experience working with a variety of machine learning techniques, including supervised and unsupervised methods, and understanding their practical strengths and limitations.
• Hands‑on experience with data wrangling techniques, including fuzzy matching, text processing, and working with large or distributed datasets.
• Familiarity with core software engineering and data science best practices such as version control, testing, logging, and reproducibility.
• Proven analytical and problem‑solving skills with a high degree of accuracy and attention to detail.
• Prior experience in regulated industries such as insurance or financial services is a plus.

Compensation

• $135,000 to $180,000 annually

Compensation is based on a range of factors that include relevant experience, knowledge, skills, other job‑related qualifications.