JOB DESCRIPTION
Position - Monitoring, Evaluation and Learning Associate
Job Location: Bengaluru, Karnataka
CTC: upto Rs 10.08 Lakhs per annum
Last Date of Application - 21st August 2026
ABOUT PHIA FOUNDATION
Partnering Hope Into Action Foundation (PHIA) is a Charitable Trust registered in Delhi in 2005. It works across multiple geographies on addressing issues which act as barriers for communities to thrive. PHIA’s focus has been on the disadvantaged and vulnerable communities who are left behind in the development interventions.
PHIA works in partnership with multiple stakeholders including government, private sector, philanthropy institutions, civil society organisations, academic institutions and community-based organisations with this focus. Its interventions and programs are spread in the states of Bihar, Jharkhand, Madhya Pradesh, Uttar Pradesh, Delhi NCR, Ladakh and Punjab. PHIA’s community centric work is on a range of issues including education, WASH, strengthening local governance, climate change adaptation, sustainable livelihoods through strengthening value chains benefiting communities, and food and nutrition security for communities.
Central to PHIA’s vision is the belief that real transformation begins at the community level. By nurturing leadership, building capacities, and advocating for systemic change, PHIA turns hope into actionable solutions. Its evidence-driven programs have touched the lives of countless individuals, inspiring them to overcome challenges and thrive.
ABOUT THE ROLE
The India Health and Climate Resilience Fellowship (IHCRF) Programme supports healthcare and public-system actors to identify, frame, prototype, test, and support adoption of human-centred solutions to climate-health and primary healthcare challenges.
IHCRF operates through a structured delivery architecture spanning Discover, Define, Develop, and Deliver phases, with explicit requirements for evidence synthesis, equity integration, stakeholder validation, design criteria, prototyping, readiness assessment, government co-ownership, and implementation learning.
IHCRF works to strengthen health systems, partnerships, and implementation pathways so that evidence-informed solutions can move from design to adoption in real district settings. Its work requires strong district engagement, careful coordination with public systems, disciplined follow-through, and the ability to convert field realities into actionable programme decisions.
The Monitoring, Evaluation and Learning Associate exists to make IHCRF’s claims about change defensible, and to make its evidence usable in time to change decisions. The role is the design authority for the programme’s measurement architecture — the Theory of Change and its versioned revisions, the MEL framework, the indicator reference sheet, the learning agenda, and the equity-disaggregation logic that every outcome and output indicator must carry — under the leadership of the Lead – Program Manager & Operations and in close partnership with the Lead – Knowledge Management, Lead – Innovation & Design, the Data Analyst, and the district Fellow pairs.
The role operates across the full delivery architecture: Strategic Foundations (Theory of Change, MEL framework, baseline equity diagnostic), Discover (sampling strategy, consent and anonymisation, Theory of Change), Define (design criteria, evaluation rubric, prototype-stage measurement plan), Develop (testing protocols, pilot data and synthesis, Theory of Change), Deliver (baseline activation, fidelity monitoring, equity-disaggregated performance review, monthly data-to-action reviews, the mid-implementation evaluation, comprehensive impact evaluation), Closeout (RE-AIM and CFIR analysis, publication pipeline, sustainability monitoring), and the Design-as-a-Service portfolio.
KEY RESPONSIBILITIES
The following responsibilities are indicative and not exhaustive. Given the adaptive and emergent nature of the programme, the role holder is expected to remain flexible and responsive to evolving organisational and programmatic priorities.
Measurement architecture, Theory of Change, and MEL framework
Author and maintain the programme Theory of Change — target populations by geography, caste, tribal status, gender and disability lens; the input → activity → output → outcome → impact chain; and the explicit boundary between where the programme’s contribution stops and the system’s begins — and revise it on evidence from v1.0 to v1.1 after Discover and to v2.0 after Develop, with documented rationale and advisory sign-off. Author the MEL framework and the indicator reference sheet covering outcome, output, process, equity, and implementation indicators, each carrying definition, disaggregation, data source, collection frequency, and responsible role. Define five to seven priority learning questions, each bound to a named decision, decision owner, and review forum. Assess baseline and routine data readiness; codify the weekly–monthly–quarterly–annual review cadence with data-flow specifications and decision-trigger thresholds.
Research and evaluation design, ethics, and data protection
Define the sampling strategy per method — purposive FGDs segmented by gender, caste, age and distance from facility; stratified random provider survey; stakeholder-informed snowball KIIs — documenting inclusion / exclusion criteria and refusal handling. Author the prototype-stage measurement plan and user-testing protocols, specifying what counts as sufficient evidence to advance, refine, or retire a prototype. Design the mid-implementation evaluation and the comprehensive impact evaluation, including equity disaggregation and IEC amendment submission where scope materially diverges from the approved Develop-phase protocol. Own the consent register and anonymisation regime, including anonymisation of participants without consent and of minors absent guardian consent, under the IEC-approved protocol, ICMR National Ethical Guidelines 2017, and DPDP Act 2023.
Evidence generation, analysis, and synthesis
Act as default accountable role for the Qualitative Coding & Triangulation SOP (SOP-ANALYSIS-CODING-001) across all FGD, KII, IDI, and observation data: apply the agreed framework, document disconfirming evidence, and triangulate across method, district, and equity segment. Lead pilot data collection against prototype-stage indicators and produce per-district and cross-district synthesis. Execute the mid-implementation evaluation and the qualitative arm of the comprehensive impact evaluation. Author the final evaluation report to be usable by advisory, donor, government, peer, and academic audiences simultaneously. Analyse implementation through RE-AIM and CFIR constructs, and develop and submit two to four peer-reviewed manuscripts, managing authorship, IEC disclosure, and conflict-of-interest declarations.
Equity integration and Equity Review gate assurance
Develop the per-district baseline equity diagnostic from NFHS-5, SECC, Census, and HMIS data, stratifying access and outcomes by caste (SC, ST, OBC, General), tribal status, gender, disability, wealth quintile, and remoteness. Embed mandatory equity disaggregation into the indicator architecture for every outcome and output indicator — as a condition of the indicator existing, not as an annexe. Produce the disaggregated evidence base that makes the first, second, and fourth Equity Review gates decidable, under the Lead – Knowledge Management’s accountability. Run the monthly equity-disaggregated performance review across caste, tribal status, gender, distance from facility, and disability where measurable, and flag inequitable reach with a remediation recommendation and a named owner, not merely a finding.
Learning Health System operations and adaptive management
Activate baseline data collection and confirm that pipelines from DHIS-2, HMIS, and field instruments are operational before implementation launch is declared. Run monthly fidelity assessment against implementation toolkit specifications through structured observation and record review, reported by facility. Convene and own the two-hour structured monthly data-to-action review in each of the four Core districts with the Fellows and the Lead – Innovation & Design, converting the month’s data into owned, dated actions and tracking closure. Specify data and dashboard requirements to the Data Analyst and review outputs for interpretive validity before they inform decisions. Build and calibrate the design criteria framework and solution evaluation rubric across effectiveness, equity, feasibility, acceptability, government adoptability, scalability, sustainability, and FCRA / legal compliance.
Design-as-a-Service portfolio quality assurance and cross-district learning
Own the cross-engagement learning and feedback loop, feeding DaaS learning from approximately ten non-Fellow districts back into the four Core districts and into programme knowledge products. Apply the DaaS quality-assurance checklist across ethics, evidence quality, equity, government ownership, capacity, and deliverable clarity. Produce the quarterly DaaS dashboard and the annual portfolio synthesis of value, patterns, failures, cost, and implications for scale. Create the weighted eligibility scoring rubric for accepting or deferring DaaS district requests. Hand over monitoring templates and coach the district focal person to own updates without programme dependence, and conduct lightweight sustainability monitoring for twelve months post-handover.
Donor, statutory, and closeout evidence support
Supply the evidence base for the quarterly donor reporting cycle, milestone-linked disbursement evidence packages, and ad hoc donor queries, under the Lead – Programme Manager & Operations’ accountability. Support source-evidence freezes for the final FCRA annual return (FC-4), the advisory and donor closeout review, and the adverse-event protocol — confirming completeness, declared quality exceptions, and version-controlled location; adverse-event chain-of-custody verification is co-held with the Lead – Programme Manager & Operations. Contribute evaluation, equity, and implementation-learning content to the final programme report, to standard documentation templates, and to the Cohort 1 retrospective that updates Fellow job descriptions on operational evidence
ESSENTIAL QUALIFICATIONS AND REQUIREMENTS
Qualifications & Experience:
- Bachelor’s or Master’s degree, or equivalent demonstrated experience, in public health, epidemiology, biostatistics, social sciences, development studies, health economics, or a related discipline; the ‘equivalent demonstrated experience’ allowance applies in full to this criterion.
- Demonstrated MEL track record on at least one programme involving an authored results framework or indicator architecture, primary data collection, and mixed-methods evaluation or synthesis.
- Strong written and verbal communication skills in English; working proficiency in one or more languages relevant to the programme geography is an advantage.
- Has authored a results framework, MEL framework, or indicator reference sheet — with definitions, disaggregation, data sources, frequency, and responsible roles — that was subsequently used to report to a funder, board, or government counterpart.
- Has designed and executed a mixed-methods evaluation end to end: sampling design, instrument development, qualitative coding and triangulation, and synthesis written for decision-use rather than for the record.
- Has worked directly with Indian routine health information systems (DHIS-2 / HMIS, NCD or programme registers) and national datasets (NFHS, SECC, Census, NSO), handling their known data-quality limitations explicitly in analysis.
- Has applied equity disaggregation — by caste, tribal status, gender, disability, wealth, and remoteness — in a way that changed a programme decision, not only a report annexe.
- Has operated under research-ethics governance: informed consent processes, IEC / IRB submission and amendment, anonymisation, and data protection consistent with the DPDP Act 2023 and ICMR National Ethical Guidelines 2017.
- Has contributed to a synthesis cycle that named disconfirming evidence and influenced a programme decision.
Core & Behavioural Competencies
- Measurement design under constraint: Builds an indicator architecture that is methodologically defensible and actually collectable by a small district team using routine systems and low-connectivity instruments — resisting both the urge to measure everything and the drift toward measuring only what is easy.
- Mixed-methods analytical judgement: Moves fluently between qualitative and quantitative evidence; triangulates across method, district, and equity segment; surfaces disconfirming evidence rather than accumulating support for a preferred reading.
- Decision-use orientation: Works backwards from a named decision, owner, and forum, and delivers evidence in the form and at the time the decision needs it. Utilisation-focused evaluation in practice, not in principle.
- Equity analysis: Detects differential reach and outcome across caste, tribal status, gender, disability, wealth, and remoteness; distinguishes a real inequity from a small-sample artefact; says so plainly when reach is failing.
- Research ethics and data-protection judgement: Recognises when a design, sampling frame, image, or dataset creates risk to participants or to the programme’s legal position, and holds the line under delivery pressure.
- Evidence and repository discipline: Freezes sources, declares quality exceptions, versions frameworks and instruments, and maintains a trail from any published claim back to its source without reconstruction.
- Ethics and inclusion: Works respectfully across differences of caste, class, gender, disability, age, and social identity while upholding safeguarding, confidentiality, and non-discrimination standards.
- Outcome orientation: Drives measurement work to decision-relevant deliverables, on the review cadence rather than after it.
- Documentation discipline: Version-controlled frameworks, frozen source lists, auditable consent and coding records.
- Collaboration: Works with Fellows, the Data Analyst, Innovation & Design, KM, and Communications without dropping coherence or duplicating their scope.
- Learning mindset: Captures method and adaptation lessons; updates the MEL framework and learning agenda with the Lead – Knowledge Management.
- Equity sensitivity: Flags equity gaps in sampling, indicator design, prototype reach, and adaptation decisions.
- Stewardship and dignity: Holds dignity, consent, and data protection as measurement standards, not as compliance overhead.
- Intellectual honesty under pressure: Reports findings inconvenient to the programme, its funders, or its government partners without softening them; distinguishes what the evidence establishes, what it suggests, and what it cannot support; states limitations before others find them.
Tools & System
Required
- Microsoft Office or Google Workspace at the level of a careful document author and reviewer.
- Routine health information systems (DHIS-2, HMIS) at the level of an active operator: extracting, interrogating, and assessing the quality of district data, including public-data tracking for post-handover sustainability monitoring.
- Qualitative analysis at the level of an active operator: NVivo, Atlas.ti, Dedoose, MAXQDA, or a documented and consistently applied manual coding framework — the codebook and audit trail are the standard, not the software choice.
- Quantitative analysis (R, Stata, SPSS, or Python) sufficient for descriptive and stratified analysis, disaggregation, and basic inferential testing.
- Mobile data capture tools (Kobo, ODK or equivalent) at the level of specifying instruments, skip logic, and enumerator account structures, and reviewing captured data.
Desirable
- Familiarity with dashboard and visualisation tools (Power BI, Tableau or equivalent) at the level of specifying requirements and reviewing outputs; dashboard construction is accountable to the Data Analyst.
- Familiarity with evidence repositories or version-control tools (Dovetail, Notion, Git).
APPLICATION PROCESS
Eligible candidates interested in this position are requested to apply through this link:
Apply with an updated resume including the names of two referees, one of whom should be your present or last Reporting Manager, by or before the last date of application. The interviews will be conducted on a rolling basis as we keep on receiving applications till a suitable candidate is found, so the applicants are advised to apply as early as possible.
Last date to apply: 21 August 2026
PHIA Foundation is an equal opportunity employer. We are committed to building an inclusive and diverse team, and we strongly encourage applications from individuals belonging to marginalized and underrepresented communities, including Scheduled Castes (SC), Scheduled Tribes (ST), minorities, women, persons with disabilities, and other gender identities.
Please note that due to the high volume of applications we anticipate receiving, only shortlisted candidates will be contacted.
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