Consultant - Public Health Data Analysis

Consultant - Public Health Impact, Epidemiology and Learning

Department: LifeScience & HealthCare

Location: Gurgaon

Job Summary

We are seeking a consultant to strengthen the health data science, evaluation and applied AI capabilities of our Social Impact Practice research desk. This is a hands-on role with responsibility for independently leading defined technical workstreams, coordinating multidisciplinary contributors, managing delivery risks and quality, and presenting findings to client stakeholders. The role does not own production engineering deployment but is responsible for technical readiness, validation evidence, documentation and effective handover.

You will conduct public health research and evaluations, analyse complex datasets and build AI prototypes to address research or programme needs. Your findings will help clients assess program performance, understand and examine inequalities and support evidence-based programme and investment decisions. This hands-on individual contributor role works with the research desk manager, domain specialists and engineering teams. Engineering specialists own production deployment.

Key Responsibilities

Health data science and analytics

• Translate public health and programme questions into analytical approaches; acquire, clean, link and analyse survey, administrative and programme datasets.
• Apply statistical and data science methods to assess trends, determinants, inequalities and programme performance. Account for survey design, missing data, uncertainty and comparability across sources.
• Develop reproducible analytical workflows, visualisations and documented datasets, with checks for data quality and source traceability.Research synthesis and evaluation design
• Conduct structured literature reviews and evidence synthesis; assess study quality and explain the strength and limitations of findings.
• Develop evaluation questions, theories of change, indicator frameworks, study designs and statistical analysis plans suited to the programme and available evidence.
• Select appropriate experimental, quasi-experimental or theory-based approaches, and distinguish programme monitoring, association, contribution and causal attribution.Primary research and field implementation
• Design quantitative surveys and qualitative studies, including sampling approaches, data collection instruments, piloting and analysis plans.
• Coordinate field implementation with programme teams and research partners, including enumerator training, progress tracking, data quality checks and resolution of implementation issues.
• Analyse primary data and integrate quantitative findings with interviews, beneficiary perspectives and implementation context. Apply relevant research ethics, consent and confidentiality requirements.Consulting delivery and technical quality
• Plan and manage technical workstreams end to end, including scope, approach, milestones, staffing inputs, dependencies, risks, quality reviews and delivery within agreed timelines and budgets.
• Clarify client questions, agree feasible methods and deliver analytical assignments to the required quality and timelines.
• Manage day-to-day client communication for assigned workstreams, including expectation-setting, scope clarification, progress updates and resolution of technical issues.
• Develop an understanding of the client’s programme context, organisational objectives and decision needs so that analytical outputs provide relevant and actionable value.
• Translate technical findings into implications, options and recommendations while clearly communicating evidence limitations.
• Identify and resolve methodological, data, implementation and stakeholder risks, escalating material issues with clear options and recommendations.
• Coordinate multidisciplinary contributors across public health, research, data science, design and engineering teams.
• Independently prepare and deliver structured, decision-oriented presentations to technical and senior client stakeholders. Prepare clear reports, briefs and presentations for technical and non-technical audiences, explaining findings, implications and limitations.
• Maintain the documentation required for research quality, contractual compliance, data governance and AI governance. Maintain documented code and research records, contribute reusable tools and methods, and provide technical inputs or peer review across desk assignments
• Guide analysts and researchers on analytical methods, code quality, evaluation design and evidence interpretation.
• Provide technical review and constructive feedback on work produced by project-team members.
• Develop reusable guidance, templates and learning sessions in areas of expertise. Support the project manager in matching tasks to required technical capabilities and identifying capability gaps. Applied AI development and evaluation
• Identify suitable AI applications and build and validate prototypes, such as predictive models, document extraction tools, evidence search assistants or research workflow agents.
• Develop working solutions using Python and relevant machine learning or language model tools; document code, data requirements, assumptions, dependencies and limitations.
• Evaluate prototypes against defined baselines and test datasets, examining accuracy, reliability, subgroup performance and failure modes, with human review appropriate to the use case.
• Design and conduct evaluations of AI adoption, usability, appropriate reliance and workflow outcomes, including changes in time, output quality and user actions. Distinguish these from downstream health outcomes.
• Prepare tested prototypes, evaluation findings and technical documentation for engineering handover and collaborate on refinements before deployment.
• Use only tools and environments approved by EVS and permitted under the relevant client contract.
• Maintain data privacy, confidentiality, source integrity and reproducibility throughout AI development and evaluation.
• Define test datasets, baselines, acceptance criteria and performance thresholds before prototype evaluation.
• Conduct and document factuality, bias, subgroup-performance, reliability, privacy and failure-mode checks appropriate to the use case.
• Maintain governance records covering data sources, prompts or configurations, assumptions, limitations, human-review points, known risks and escalation decisions.
• Define fallback or continuity arrangements where prototype failure or AI-service unavailability could affect delivery.
• Document the effect of AI-enabled solutions on turnaround time, effort, output quality and client or user workflows.

Practice and business contribution:

• Contribute technical inputs, methods, effort estimates, risks and differentiators to proposals and concept notes.
• Identify opportunities where health analytics, evaluation or applied AI could extend an existing assignment or address an adjacent client need.
• Develop reusable methodologies, examples and technical content that support knowledge management, capability development and the sales cycle.
• Support demonstrations, thought starters and client discussions relating to health data science, evaluation and responsible AI.

Minimum education

• A master’s degree or higher in Data Science, including Health Data Science. Formal training in public health, including epidemiology, biostatistics and research methods, through the qualifying degree or additional structured coursework. Candidates from either degree pathway must demonstrate applied health analytics and AI development capability.

Essential experience and skills

• 5 -7 years of professional experience, with substantial relevant work in public health research, health data analysis or programme evaluation.
• Hands-on proficiency in Python for data analysis and AI prototyping, SQL for data extraction and management, and statistical analysis using Python, R or Stata.
• Demonstrated experience in secondary-data analysis, evidence synthesis, evaluation design and primary research, including practical responsibility for field implementation and data quality.
• Experience building and validating a working AI prototype or application, with the ability to explain personal contributions, testing methods, findings and limitations.
• Experience assessing AI tools in use, including adoption, user behaviour or workflow outcomes, alongside technical performance.
• Sound judgement in selecting methods, interpreting uncertainty and communicating what the evidence can support; strong analytical writing and stakeholder communication.
• Ability to independently manage technical assignments and collaborate effectively with public health specialists, programme teams, field partners and engineers.

Preferred experience

• Familiarity with Indian health surveys and information systems, such as NFHS, HMIS and SRS, and experience working with government, donors, nonprofits or public health research organisations.
• Experience in one or more relevant areas, such as immunisation, maternal and child health, nutrition, infectious diseases, health systems or digital health.
• Experience with retrieval-augmented generation, language model APIs, research agents or workflow automation; familiarity with Git and technical handover practices.
• Experience with geospatial analysis, mixed methods research, behavioural or human-centred evaluation, or visualisation tools such as Power BI or Tableau.

Disclaimer: The following job description serves as an informative reference for the tasks you may be required to perform. However, it does not constitute an integral component of your employment agreement and is subject to periodic modifications to align with evolving circumstances.

Please Note: We appreciate the accuracy and authenticity of the information you provide, as it plays a key role in your candidacy. As part of the Background Verification Process, we may verify your employment, education, and other details. Please ensure all information is factual and submitted on time. For any assistance, your recruiter is available to support you.