Summary:
In this role, you will develop and deploy advanced analytics, machine learning, generative AI, and agentic AI solutions that address complex business and client challenges. You will design scalable data science products, translate sophisticated analytical findings into actionable insights, and guide data scientists in delivering high-quality solutions that improve clinical, operational, and economic outcomes. You will influence technical strategy, drive innovation, and ensure analytical solutions align with organizational objectives.
Qualifications:
Relevant degree preferred. Advanced degree in applied mathematics, statistics, computer science, econometrics, or a related field is a plus.
7 or more years of relevant experience required.
Demonstrated expertise in data science methodologies, statistical modeling, machine learning, generative AI, agentic AI, and natural language processing required.
Strong understanding of large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and autonomous decision-making frameworks within enterprise AI environments highly preferred.
Expertise in programming languages such as Python, R, SQL, SAS, or similar analytical tools.
Experience with vector databases, semantic search technologies, knowledge graphs, metadata management, or enterprise search platforms.
Experience deploying, monitoring, and maintaining machine learning and AI solutions in production environments.
Knowledge of responsible AI principles, model governance, explainability, bias detection, validation methodologies, and ethical use of data.
Experience designing and implementing human-in-the-loop workflows that balance automation, governance, risk management, and user oversight.
Experience working with cloud-based and distributed data platforms and modern analytics frameworks such as Databricks.
Strong analytical, problem-solving, communication, and presentation skills with the ability to convey complex concepts to diverse audiences.
Experience leading complex analytics initiatives, providing technical guidance, and mentoring data scientists.
Experience working with healthcare data, including clinical, claims, operational, or financial datasets preferred.
Responsibilities:
Lead the design, development, and implementation of advanced machine learning, generative AI solutions across structured and unstructured data.
Build and deploy scalable, automated data science products and reusable analytic assets that support business and client objectives.
Analyze large-scale, high-dimensional datasets using modern analytics tools and distributed computing platforms to identify trends, opportunities, and actionable insights.
Design and prototype agentic AI workflows leveraging large language models (LLMs), retrieval systems, structured data, APIs, tools, and business rules to automate complex business processes.
Translate business requirements into AI agent architectures, including task decomposition, tool orchestration, routing logic, escalation paths, and human approval checkpoints.
Optimize retrieval-augmented generation (RAG) solutions through embedding evaluation, metadata design, reranking approaches, citation quality assessment, and knowledge freshness validation.
Develop evaluation frameworks for machine learning and AI solutions, including performance, accuracy, reliability, hallucination risk, latency, cost, safety, and consistency measures.
Make key analytical and architectural decisions, establish technical standards, and provide leadership on methodology, model selection, solution design, and AI implementation best practices.
Lead code reviews, mentor data scientists, and promote best practices in software development, responsible AI, model governance, and analytics delivery.
Partner with engineering, platform, and business teams to deploy, monitor, and maintain production-ready analytics and AI solutions.
Translate complex analytical findings into clear reports, visualizations, and presentations for technical and non-technical audiences.
Collaborate with stakeholders and clients to define analytical approaches that address strategic, operational, and clinical objectives.
Evaluate emerging technologies and industry trends to advance organizational analytics and AI capabilities.
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