About this role
About Narayana Health:
Narayana Health is headquartered in Bengaluru, India, and operates a network of hospitals in India and Overseas. Our mission is to deliver high-quality, affordable healthcare services to the broader population. Narayana Health Group is Indias leading healthcare provider and one of the largest hospital groups in the country, with a network of 21 hospitals, 5 heart centres, and 19 primary care facilities. The NH group treats over 2.6 Million patients every year from over 78 countries, covering 30+ medical specialities.
For more details, please refer to our website at: https://www.narayanahealth.org (https://www.narayanahealth.org/)
About Medha:
Medha is the Advanced Analytics & AI arm of Narayana Health, focused on transforming healthcare through data , intelligence, and thoughtfully designed digital products. Our mission is to simplify decision‑making across hospitals, clinics, and care teams by bringing clarity, precision, and actionability to every layer of healthcare operations.
Our products power large health systems, influence mission‑critical workflows, and support clinical and non‑clinical teams in delivering safer, faster, and more coordinated care.
For more details, please refer to our website at https://www.medha.health (https://www.medha.health/)
Job Purpose:
• Enable business teams across departments to understand their performance, identify growth levers, and uncover opportunities for optimization.
• Drive business performance and potential through the effective use of existing and new data sources, analytical tools, and techniques — including AI agents, bots, automation, data platforms, and advanced analytics.
Key Responsibilities:
Analytics & Dashboarding:
- Ideate, develop, and continuously refine comprehensive analytical models and dashboards for different business verticals.
- Build models to identify, quantify, and track key drivers and levers of business performance.
- Design clear and effective visualizations that translate analytical outputs into actionable insights and support decision-making.
- Translate analytical requirements shared by Product Managers into structured analytical frameworks, models, KPIs, metrics, and measurable deliverables.
- Apply statistical and predictive techniques, such as forecasting and anomaly detection, where relevant to strengthen analysis and recommendations.
AI Agent & Bot Development:
- Design, build, and deploy conversational and non-conversational AI agents and bots to automate business processes, analytics workflows, and reporting. This is a significant and growing part of the role.
- Identify opportunities for AI-driven automation across the business and own implementation end-to-end, from ideation through deployment and adoption.
- Proactively identify automations that can reduce delivery turnaround time (TAT), improve output quality, and increase analytical productivity.
Collaboration & Continuous Improvement:
- Collaborate closely with Product Managers, who act as the bridge to the business, to understand analytical requirements, tracked metrics, and expected outcomes for each vertical.
- Stay current with emerging analytics, data , AI, and automation tools, and evaluate opportunities to apply them to business problems.
- Operate with a high degree of ownership and latitude, developing creative and practical solutions to complex business challenges.
Candidate Requirements:
Education: B.E. / B.Tech / M.Sc. in Computer Science, Statistics, Data Science, or another related quantitative field.
Experience: 0 – 2 years of relevant experience in data analytics, business analytics, product analytics, or a closely related field.
Key Skills:
- Strong business analysis skills, with the ability to translate analytical requirements from Product Managers into clearly defined analytical problems, frameworks, and deliverables.
- Strong logical and analytical reasoning, with the ability to break down complex and ambiguous problems methodically and think structurally.
- Ability to think strategically, handle ambiguity, and work effectively in a fast-paced, limited-structure, multicultural environment.
- Proven ability to work independently, manage multiple priorities, and set and achieve targets with limited guidance.
- Creative problem-solving mindset, with the ability to identify opportunities beyond assigned tasks.
- Strong attention to detail and organizational skills.
- Ability to collaborate effectively with cross-functional teams.
- Good written and verbal communication skills.
- Curiosity and adaptability to quickly learn and adopt new data , analytics, AI, and automation tools as the technology stack evolves.
- Proactive mindset toward identifying and implementing AI-driven automation opportunities.
Technical Skills:
- SQL / Query Languages — Must Have: Strong SQL skills, including the ability to query large-scale and distributed data platforms such as Trino/Presto and Spark SQL, in addition to traditional relational data bases.
- Python — Must Have: Strong Python skills for data analysis and automation, including pandas and NumPy, with the ability to write clean, reusable, and maintainable scripts.
- AI / GenAI & Automation: Working knowledge of AI/GenAI tools and automation for analytics, including LLM-based copilots for querying data , prompt-driven analysis, AI agents, and workflow automation for recurring reports and insight generation.
- AI Agents / Bots: Experience building conversational solutions such as chatbots or voice/text assistants and non-conversational rule-based or workflow-based bots and automation solutions is a plus.
- Statistics / Machine Learning: Exposure to statistical or machine learning techniques such as forecasting, regression, clustering, and anomaly detection is a strong plus.
- Version Control & Collaboration: Familiarity with Git, Azure DevOps, or similar collaborative development tools is a plus.
- BI & Visualization: Experience with Power BI, Tableau, Qlik, or similar visualization/BI tools, along with comfort working with modern custom-built dashboarding and BI platforms.
- Excel: Strong Excel skills, including advanced formulas, pivot tables, data modeling, and analytical reporting.
What Success Looks Like:
- Business teams have reliable, decision-ready dashboards and metrics.
- Analytical requirements are converted into clear KPIs, models, and actionable insights.
- Recurring analytics and reporting workflows are progressively automated using AI and other technologies.
- Data -driven recommendations contribute to measurable improvements in business performance, operational efficiency, and healthcare quality.
- The analyst proactively identifies new opportunities for analytics, automation, and AI rather than operating only
- as a reporting function.