Data Scientist - ML and Advanced Analytics

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.
• 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.

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).
• 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

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