Clinton Health Access Initiative, Inc.
Website:
clintonhealthaccess.org
Company:
https://www.linkedin.com/company/clinton-health-access-initiative
Seniority: Not Applicable
Industries: Non-profit Organizations
Job details:
WJCF is an Indian non-profit organisation committed to saving lives by reducing the burden of disease and strengthening government-owned, high-quality health systems. Since 2007, WJCF has combined data-driven approaches and deep public health expertise with strong government partnerships to design, implement, and scale solutions across India's national and state health programmes. We work for and at the service of governments, supporting the Ministry of Health & Family Welfare and State Departments of Health to build systems that are strong, sustainable, and led by Indian institutions.
As an Indian organisation, WJCF brings an unmatched depth of contextual understanding of India's health system architecture, policy landscape, and implementation realities across diverse geographies and population groups. This local institutional credibility, combined with global technical rigour, is central to WJCF's effectiveness as a partner to governments and donors.
Our work is built around four complementary roles as a Trusted Government Partner, co-designing programmes and strengthening health system architecture; as an Operational Partner, translating strategies into effective on-the-ground delivery; as a Market Shaper, improving the availability and affordability of health commodities; and as an Ecosystem Catalyst, convening governments, development partners, academia, and the private sector to drive solutions at scale.
WJCF's programme portfolio spans thematic areas like hepatitis, HIV/AIDS, tuberculosis, vector-borne diseases, syphilis, cervical cancer, diabetes, maternal and childhood anaemia, immunisation, under-5 diarrhoea and pneumonia, sexual and reproductive health, Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB PM-JAY), Ayushman Bharat Digital Mission (ABDM), hypoxemia and oxygen, safe drinking water, sickle cell disease, presbyopia, lead poisoning, and cross-cutting thematic areas like AI and Health, integrated disease surveillance, and climate and health.
We currently support programmes across 19 states and union territories, with teams working at national, state, district, and sub-district levels.
Our people are our greatest asset. WJCF brings together a talented, diverse team of professionals from public health, analytics, consulting, healthcare, the development sector, and academia, all united by a shared commitment to improving health outcomes for the people of India. We are entrepreneurial, action-oriented, and deeply grounded in the communities and systems we work in. Our field teams collectively bring hundreds of years of experience managing public health programmes across the country.
WJCF collaborates with a range of international and domestic partners and donors to advance its mission, including an affiliation with the Clinton Health Access Initiative (CHAI), a global health organisation with which WJCF shares a common mission and values.
Project Background
Over the past seven years, Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB PM-JAY) has become one of the largest health-assurance programmes in the world. It has issued more than 44 crore Ayushman cards and authorised over 11.1 crore hospital admissions worth around ₹1.73 lakh crore, across a network of more than 38,000 empanelled hospitals. Running at this scale, the programme has generated a large volume of data as a by-product of its everyday operations, built up over seven years of registrations, pre-authorisations, claims, and utilisation records. Making sense of data this size has never been easy, but Recent advances in artificial intelligence (AI) now provide an opportunity to derive meaningful insights from this rich repository of data more systematically, robustly, more quickly and in a manner that is more actionable than previously possible or conceivable. This in turn provides a clearer picture of how the scheme is working, and of the ways in which even further value can be delivered to citizens by the scheme, giving the National Health Authority (NHA) a real opportunity to refine the programme and sharply improve its effectiveness.
The NHA, an attached office of the Ministry of Health and Family Welfare with full functional autonomy, implements AB PM-JAY and also drives the Ayushman Bharat Digital Mission (ABDM), now a mature layer of national digital health infrastructure that includes registries such as the Health Facility Registry and the Healthcare Professionals Registry and platforms such as the National Health Claims Exchange (NHCX). ABDM follows a federated architecture built on privacy and security by design, which means there is no central store of health records. A patient's clinical records stay with the facility that created them and move only as encrypted FHIR bundles, with the patient's consent, so the NHA never looks inside an individual's health record. What it can work with is aggregated and metadata-level information, for example how actively facilities and providers are participating, or how registration compares with actual use, and even this is enough to reveal patterns that help strengthen the programme.
Turning this potential into practical insight needs dedicated, specialised capacity. The Health Analytics and AI Unit (HAAU) is the NHA's in-house capability for advanced analytics and AI, set up to do exactly this. WJCF supported the NHA in preparing the HAAU Vision Document and, through a team embedded at the NHA, has helped take it from vision to delivery, producing analyses and tools that are already changing how the NHA uses its data. WJCF is now expanding the HAAU with a set of specialised roles to accelerate delivery and catalyse the development of institutionalised capability within the NHA. All of this work is done with privacy by design and strong data governance, in line with the Digital Personal Data Protection (DPDP) Act 2023 and the Strategy for Artificial Intelligence in Healthcare for India (SAHI).
Position Summary
WJCF is looking to hire an Applied AI Associate (Public Health) within the Health Analytics and AI Unit (HAAU), embedded within the NHA and reporting to the HAAU Lead. This is a hands-on role for someone who combines the understanding of the public-health domain and applied AI and machine-learning skills, and who can help translate the NHA's programmatic priorities into technical requirements and working solutions.
The individual will enable the development and operationalization of pipelines and tools that turn analytical designs into working systems, including AI-assisted data structuring and validation, anonymisation and synthetic-data generation, reverse extract-transform-load (ETL) integration of analytical outputs into operational systems, and a hardened, production natural-language querying capability. As part of the HAAU team, the Applied AI Associate will work closely with the unit's programmatic and health-financing skills, handling simpler requirements directly and preparing the data and tooling that more complex builds and financing-framework analyses need.
This position is based out of the NHA office in New Delhi, so that the individual stays closely aligned with the NHA's immediate priorities and the rapidly evolving digital health landscape. This role provides an excellent opportunity for an applied AI professional who would like to apply modern AI to India's flagship health protection and digital health programmes.
- Help translate the NHA's programmatic priorities and hypotheses into clear technical requirements, handling simpler requirements directly and relaying larger builds to the unit's specialised data-science skills.
- Help the team assemble and prepare the data needed for analytics and insights generation.
- Build and operate the pipelines and tooling that turn analytical designs into working systems, ensuring everything is scalable, monitored, and reproducible.
- Develop workflows for AI-assisted data structuring and validation at source (including clinician-in-the-loop review mechanisms) to improve quality of programmatic data.
- Build de-identification and anonymisation pipelines that yield high-quality analytical datasets aligned with the DPDP Act 2023 and SAHI, and generate synthetic and augmented data where real records are sparse or too sensitive to share.
- Support the hardening of a production natural-language querying capability that lets NHA officials and state teams interrogate aggregated programmatic data in plain language.
- Apply model-monitoring, drift-detection, and reproducibility discipline across the tools the unit builds. This would include building evaluation harnesses and curated benchmark / golden datasets, defining task-specific accuracy metrics and acceptance thresholds, running offline evaluation before release and online / A-B evaluation after.
- Perform other responsibilities as requested by WJCF and NHA leadership.
- Bachelor's or master's degree in computer science, data science, statistics, public health, or a closely related field.
- Minimum 5 years of experience in building ML/ AI systems, taking them from prototype to production and maintaining them in live production (serving real users, with monitoring, versioned releases, and a rollback path, end-to-end).
- Hands-on experience evaluating and benchmarking LLM-based systems designing evaluation sets, defining accuracy and quality thresholds, and running regression testing so that tools are released and scaled on the basis of measured performance.
- Experience building retrieval-augmented generation (RAG) systems, including embeddings and vector stores, to let users interrogate data and draw on existing analyses in natural language.
- Experience with anonymisation and de-identification pipelines and, ideally, synthetic-data generation.
- Comfort with large-scale data processing (distributed / columnar warehousing, batch + incremental pipelines) and query and cost optimisation over large data.
- Understanding of the public-health domain, sufficient to work with programmatic data.
- Proficiency in Python and the mainstream ML stack; strong SQL.
Preferred
- Experience with reverse-ETL and operational integration of model outputs.
- Cloud ML platform experience (AWS, Azure, or GCP).
- Machine-learning operations (MLOps) practices and containerisation basics.
- Explainability frameworks for regulated or policy environments.
- Prior healthcare, insurance, or public-sector work.
Skills & Traits
- Ability to translate programmatic questions into model-ready problems and working tools.
- Excellent analytical (qualitative and quantitative) and communication (written and verbal) skills.
- Ability to think strategically, handle ambiguity, and problem-solve in a fast-paced, limited-structure, multi-stakeholder environment.
- A focus on building institutional capability rather than creating personal dependence.
- Hands-on technical fluency in Python, SQL, and ML libraries (scikit-learn, PyTorch or TensorFlow, XGBoost), LLM and OCR tooling, Docker, experiment-tracking (MLflow or Weights and Biases), cloud ML platforms, and Git.
- Familiarity with LLM guardrails and observability, prompt and response logging, output validation, and hallucination and failure-mode testing. Alongside classical model-monitoring and drift-detection discipline.
- A working understanding of responsible-AI practice fairness and bias assessment, explainability, and human-in-the-loop design, consistent with SAHI's lifecycle, risk-proportionate approach to AI in healthcare.
- Impeccable integrity; humility and open-mindedness; a learning mentality; tenacity and resourcefulness.
- Fluency in English. Fluency in Hindi or an additional Indian language is an advantage.
Last Date to Apply 9th September, 2026
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