Senior Data Scientist

Job Description

The Senior Data Scientist will be a key member of the Data Science and AI team within

the Chief Data Officers department, grounded in practical healthcare domain

experience and focused across three core priorities.

The first priority is AI Governance, including the development and maintenance of

governance process documentation, standards, responsible AI practices, risk and

feasibility assessments, intake support, review materials, and alignment with enterprise

AI strategy. The second priority focuses on AI performance, value realization, and cost

optimization across enterprise AI initiatives. This includes measuring and improving

solution performance, defining and tracking business-value metrics, identifying

opportunities to reduce AI-related run costs, improving efficiency of AI-enabled

workflows, and helping ensure AI investments produce measurable operational,

financial, and stakeholder value. The remaining priority involves consulting with and

supporting the data science and AI needs of other teams across the enterprise. In this

capacity, the Senior Data Scientist will partner with the spokes of the AI NOW initiative

to help execute AI NOW use cases identified through a prioritization process, helping

teams convert prioritized ideas into clear problem statements, data requirements,

analytical approaches, success criteria, implementation plans, validation plans, and

practical paths to adoption.

Responsibilities

• Develop and maintain governance processes, SOPs, standards, templates,

review checklists, and decision-support materials for enterprise AI initiatives.

• Support responsible AI practices by helping assess AI use cases for business

value, data readiness, model risk, privacy and security considerations, operational

feasibility, and alignment with enterprise architecture and governance expectations.

• Partner with AI Governance, Data Science & AI leadership, technology teams,

and business stakeholders to ensure AI opportunities are evaluated consistently and

routed through appropriate intake, review, prioritization, and implementation

pathways.

• Define, measure, monitor, and communicate the performance, value

realization, and cost efficiency of enterprise AI initiatives.

• Develop and maintain practical performance scorecards, value realization

frameworks, KPI definitions, adoption metrics, cost baselines, cost tracking

approaches, and reporting assets that help leaders understand whether AI solutions

are performing as intended and generating measurable value.

• Partner with business, technology, finance, product, operations, and analytics

stakeholders to translate AI use cases into clear expected outcomes, benefit

hypotheses, measurement plans, operating metrics, and post-implementation value

tracking routines.

• Analyze AI solution performance across business impact, user adoption,

workflow efficiency, model or system quality, operational effectiveness, cost-to-

serve, compute or platform consumption, and ongoing support needs.

• Identify opportunities to optimize AI-related costs, including cloud or

platform consumption, model usage patterns, data processing intensity, licensing

utilization, workflow design, retraining or monitoring overhead, and avoidable

manual effort.

• Support continuous improvement of deployed or in-flight AI solutions by

surfacing performance gaps, cost drivers, adoption barriers, and value leakage, then

recommending practical remediation actions in partnership with responsible teams.

• Consult with and support other teams data science and AI needs, with a

particular focus on enabling the spokes of the AI NOW initiative to execute high

priority use cases identified through the Weighted Shortest Job First (WSJF)

process.

• Collaborate with business unit and functional teams to translate prioritized

AI NOW opportunities into clear problem statements, data requirements, analytical

approaches, success metrics, implementation plans, and executable proof-of-

concept or production-ready workstreams.

• Apply machine learning, natural language processing, generative AI,

statistical modeling, and advanced analytics techniques to support feasible use cases

across structured, semi-structured, and unstructured healthcare data sources.

• Extract, analyze, and interpret data from relational databases and cloud data

platforms using SQL, Snowflake, Python, R, and related tools; develop compelling

visualizations or dashboards using Power BI or similar tools when needed to

support stakeholder decision-making.

• Provide hands-on consulting and execution support to help teams move AI

NOW use cases from idea intake and prioritization into experimentation, validation,

implementation, monitoring, value realization, and responsible scaling.

• Document reusable patterns, lessons learned, implementation playbooks,

value measurement methods, cost optimization guidance, governance artifacts, and

technical recommendations that help scale AI adoption responsibly and efficiently

across the enterprise.

• Collaborate with internal stakeholders across product, operations,

technology, analytics, clinical, and business teams to ensure AI solutions are

practical, governed, measurable, and aligned with enterprise priorities.

Required qualifications

• Bachelor’s degree in computational, quantitative, scientific, or healthcare-

related discipline such as computer science, data science, artificial intelligence,

biomedical informatics, statistics, applied mathematics, biomedical engineering,

public health, clinical informatics, or equivalent demonstrable experience.

• At least 3 to 4 years of hands-on experience in healthcare data science,

healthcare analytics, AI/ML, clinical informatics, or a closely related healthcare

domain, with demonstrated ability to work with healthcare data and translate

business or clinical needs into analytical solutions.

• Experience working with healthcare data sources such as EMR/EHR,

oncology data, medical claims, pharmacy claims, remits, genomics, population

health, HEOR, clinical documentation, or other real-world healthcare datasets is

required or strongly preferred depending on project needs.

• Practical experience applying machine learning, natural language processing,

generative AI, statistical modeling, or advanced analytics techniques to solve

healthcare or healthcare-adjacent business problems using structured, semi-

structured, and unstructured data.

• Experience supporting AI Governance, responsible AI, model risk

assessment, data governance, technology intake, compliance-oriented

documentation, standards development, or similar enterprise governance processes

is strongly preferred.

• Experience defining, measuring, and communicating AI or analytics

performance through KPIs, dashboards, scorecards, adoption metrics, operating

metrics, value realization frameworks, cost baselines, or post-implementation

measurement routines.

• Experience analyzing technology, analytics, cloud, or AI-related cost drivers

and identifying opportunities for cost optimization, productivity improvement,

workflow efficiency, licensing utilization, platform efficiency, or operational value

capture.

• Ability to evaluate AI use cases through a healthcare-aware lens that

considers business value, patient/provider and stakeholder impact, data readiness,

technical feasibility, privacy and security considerations, implementation

complexity, performance monitoring needs, cost-to-operate considerations, and

measurable outcomes.

• Experience consulting with cross-functional stakeholders to define use cases,

clarify requirements, document problem statements, identify data needs, assess

feasibility, define success metrics, and support execution of data science or AI

solutions.

• Proficiency with SQL and at least one analytical programming language such

as Python or R. Experience with Snowflake, cloud platforms, Power BI, and modern

AI/ML development environments is preferred.

• Strong documentation and communication skills, including the ability to

create governance artifacts, SOPs, intake materials, value realization materials,

performance summaries, cost optimization findings, implementation playbooks,

executive summaries, technical findings, and stakeholder-ready recommendations.

Preferred Qualities

Proactive, collaborative, consultative, structured, curious, pragmatic, detail-

oriented, healthcare-domain oriented, value-focused, cost-conscious, comfortable

working across teams, and able to balance governance rigor, practical execution,Role & responsibilities

Preferred candidate profile if anyone interested please share your updated resume to

karthik.p@kanarystaffing.com