Senior Data Scientist

Kanary Staffing; Bengaluru; Hybrid; INR 1,800,000 - 3,200,000; Full time

Job summary

Kanary Staffing is seeking a seasoned Healthcare AI Data Scientist to help assess AI use cases, measure value, and optimize costs across enterprise data sources. You will work with business, technology, finance, and analytics teams to translate needs into analytical solutions and governance patterns.

The role emphasizes responsible AI, data privacy, and enterprise architecture alignment, leveraging SQL, Python, and BI tools to drive measurable outcomes in healthcare settings.

Qualifications

• 5+ years of hands-on experience in healthcare data science, analytics, or AI/ML.
• Experience with healthcare data sources (EMR/EHR, claims, genomics) is preferred.
• Ability to translate business or clinical needs into analytical solutions.
• Proficiency with SQL and at least one analytics language (Python/R).

Responsibilities

• Assess AI use cases for business value, data readiness, risk, privacy and security considerations.
• Partner with technology and business stakeholders to ensure AI opportunities are evaluated and routed through intake, review, prioritization, and implementation pathways.
• Develop and maintain performance scorecards, KPI definitions, adoption metrics, and cost-tracking approaches.
• Analyze AI solution performance across business impact, user adoption, and operational efficiency.
• Identify cost optimization opportunities related to cloud or platform consumption and data processing.
• Document reusable patterns, playbooks, and governance artifacts to scale AI responsibly.

Skills

Healthcare data science

AI/ML

Clinical informatics

Data analytics

Education

Bachelor's degree in computational/scientific/healthcare-related field

Tools

SQL

Python

R

Snowflake

Power BI

Job description

Role & responsibilities

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 standards.

• Partner with technology teams, and business stakeholders to ensure AI

opportunities are evaluated consistently and routed through appropriate intake,

review, prioritization, and implementation pathways.

• Develop and maintain practical performance scorecards, value realization

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

approaches 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.

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

LLMOps, 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, process 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,

measurable, and aligned with enterprise priorities.

Preferred candidate profile

Bachelors 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 5 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 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.