Senior Data Scientist - Network Analytics & AI Enablement
Indexed description
The aim of these roles is to apply advanced analytics to lower the cost of healthcare for our members, while improving quality and access.
These are hands-on roles designed for solid builders who want to increase scope over time, owning key workstreams today and growing toward end-to-end technical leadership with mentorship from Principal Data Scientists and Data Science Directors.
You will operate with a high degree of autonomy to deliver high-impact analytics by framing problems, developing approaches, coding solutions, and partnering with engineering and business teams to implement solutions in production and operations.
Because we have multiple openings across two teams, candidates do not need to have experience in every responsibility listed below. We are open to candidates with depth in some areas and interest in expanding into others.
Careers with Optum offer flexible work arrangements and individuals who live and work in the Republic of Ireland will have the opportunity to split their monthly work hours between our Dublin or Letterkenny office and telecommuting from a home-based office in a hybrid work model.
Primary Responsibilities
- Build, test, and deploy analytics/data science solutions through hands-on coding, owning meaningful workstreams that deliver measurable improvements in healthcare cost, quality, and access
- Build and use LLM-assisted workflows (prompt engineering, retrieval, human-in-the-loop review) and implement evaluation practices (test cases, automated checks, monitoring) to validate correctness and consistency of outputs
- Write maintainable, production-quality Python/R and SQL, contributing to pipelines, tools, and models, and partnering with data/software engineering to operationalize solutions
- Partner with business stakeholders to translate questions into clear analytical problem statements, success metrics, and delivery plans; communicate progress and results in a concise, actionable way
- Take joint ownership with Data Engineering for the availability, quality, and enrichment of our data
- Identify and assess analytics and ML opportunities, define requirements and acceptance criteria, and contribute to solution design aligned with team goals
- Apply advanced analytics techniques (e.g., anomaly detection, pattern recognition, forecasting, segmentation) to surface emerging risks/opportunities and inform interventions
- Contribute to solid team practices through code reviews, sharing best practices, and knowledge-sharing
- Demonstrate solid curiosity and energy for emerging data science and applied AI capabilities, learning new techniques, sharing learnings with the team, and translating promising approaches into practical improvements
- Continue building domain expertise in healthcare and the U.S. healthcare system to ensure solutions are context-aware and impactful
Required Qualifications
- Undergraduate degree in mathematics, statistics, actuarial science, data science, physics, computer science, engineering or related quantitative area
- Proven extensive experience with SQL and R/Python or other analytics languages
- Demonstrated experience using programming to solve complex technical problems
- Proven experience applying analytics and data science in a business context to drive insights and outcomes
- Demonstrated effective communication skills
- Master's degree, PhD in a quantitative field or relevant professional qualification
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