TERMS OF REFERENCE

Manager - Data

and Insights

Organisation: CMS

Location: Delhi/Gurgaon, with regular in-person participation in programme meetings and flexibility to work from the programme office as required

Reporting: Lead, Catalyst.X-CMS

Preferred start: 1^st September 2026

1. Programme context

CMS works at the intersection of programme strategy, data, technology, design and decision support. It supports large-scale programmes operating across multiple partners, locations and implementation models, where information is often spread across systems and teams and must be brought together for practical use.

The requirement is therefore broader than conventional monitoring and reporting. The programme needs someone who is comfortable working directly with detailed datasets, can spot inconsistencies and patterns, understands how digital systems and data flows work, and can translate evidence into intuitive dashboards, visual stories and management conversations.

The role will help build this bridge between row-level data and programme-level decisions - combining MEL discipline with technology fluency, analytical curiosity, visual judgement, creativity and close engagement with teams.

2. Purpose of the role

The Manager - Data and Insights will be embedded within the programme engagement and will own the day-to-day connection between programme questions, detailed data, digital systems, analysis and communication. The person will work closely with programme teams, CMS specialists and implementation partners to make data reliable, understandable and useful.

This is not a reporting-only or coordination-only MEL position. It requires someone who enjoys getting into the data personally, working through messy details, testing ideas, improving how information is captured and presented, and finding creative ways to explain what the evidence is saying. The role combines MEL thinking, hands-on analytics, data visualisation and storytelling, digital product ownership, quality assurance and team engagement.

What success looks like

·The person can move confidently from a row-level data issue to the wider programme narrative without losing accuracy or context.

·Datasets, indicators and dashboards are trusted because definitions, calculations and anomalies are examined carefully and resolved.

·Programme reviews use clear visuals and evidence-led narratives to explain what is changing, why it matters and what should happen next.

·The MIS reflects programme logic, is intuitive for users and is improved through close observation of how teams actually work.

·Programme and partner teams actively engage with the evidence because the role makes data accessible, relevant and useful to them.

3. Key responsibilities

3.1 Hands-on data management and analysis

·Work directly with programme datasets at a detailed level, including cleaning, joining, restructuring, validating and analysing data from multiple partners and systems.

·Build and maintain practical analysis files, reusable templates, data dictionaries and documented calculation logic so that outputs are reproducible and auditable.

·Interrogate unusual values, missing information, inconsistent totals and off-trend results rather than accepting aggregated reports at face value.

·Undertake exploratory and focused analyses to identify patterns, bottlenecks, outliers, emerging questions and areas requiring deeper investigation.

·Move comfortably between raw data, summary metrics and programme context while preserving a clear line back to the source.

3.2 MEL architecture and data logic

·Maintain and refine the programme results framework, indicator matrix and reporting architecture in alignment with the programme's Theory of Change and management priorities.

·Develop clear indicator reference sheets covering definitions, formulas, disaggregations, sources, verification, frequency, ownership and quality checks.

·Map programme indicators, targets and reporting responsibilities to relevant partners, geographies and implementation models.

·Support target-setting and revision by testing assumptions against historical performance, operating context, partner capacity and implementation conditions.

3.3 Technology and MIS product ownership

·Translate programme questions and user needs into clear workflows, business rules, data structures, validation logic, user stories and acceptance criteria for technology and data teams.

·Map partner formats into a common data structure and support master-data preparation, migration and standardisation across organisations and geographies.

·Lead programme-side testing of forms, calculations, QC workflows, dashboards and exports; document defects precisely and verify fixes before release.

·Act as a confident super-user of the MIS and related digital tools, supporting configuration, user onboarding, reporting cycles and routine problem-solving.

·Maintain a prioritised issue and change log and work constructively with developers, designers and data specialists through iterative improvements.

3.4 Data quality and attention to detail

·Own the reporting calendar and monitor completeness, timeliness and consistency across partners and geographies.

·Develop practical quality protocols covering automated checks, anomaly review, source verification, physical validation and correction approval.

·Trace errors to their source, coordinate resolution with programme and partner teams, and maintain an auditable correction trail.

·Check every output for calculation errors, denominator mismatches, misleading labels, inconsistent filters and gaps between the data and the narrative.

·Produce concise data-quality reports that distinguish isolated errors from recurring process or system problems.

3.5 Data visualisation, storytelling and decision support

·Analyse programme performance by partner, geography, cohort and implementation model, including trends, target variance and operational bottlenecks.

·Design clear, intuitive charts, dashboards and review packs that help different audiences see patterns and act on them quickly.

·Combine quantitative evidence, qualitative insights and field context into concise narratives that explain what is happening, why it matters and what questions remain.

·Experiment with new visual and narrative formats for leadership reviews, programme teams, partners, funders and other stakeholders.

·Respond to time-sensitive analytical questions with accurate, decision-ready outputs rather than generic data dumps.

3.6 Team engagement, learning and creative problem-solving

·Build strong working relationships with programme and partner teams so that questions, uncertainties and data issues surface early.

·Facilitate evidence-based reviews that move naturally from insight to decisions, named actions, timelines and follow-up.

·Identify positive deviance, persistent underperformance and emerging risks, and help teams test explanations and response options.

·Use curiosity and creativity to design rapid analyses, assessments or learning deep-dives when routine information is insufficient.

·Make technical concepts accessible to non-technical colleagues and create space for teams to challenge, interpret and use the evidence.

3.7 Field grounding, capability building and institutionalisation

·Undertake periodic field visits with programme, regional and field teams to understand how data is generated, test whether tools reflect field practice and triangulate reported results.

·Design practical onboarding, guidance and hands-on support on data entry, quality control, reporting, dashboards and interpretation.

·Observe how teams use tools and outputs, then simplify workflows, improve interfaces and adapt support based on real user behaviour.

·Strengthen programme and partner capability so that data quality, analysis and use improve at source rather than only during central consolidation.

·Document definitions, decisions, workflows, analytical logic and learning so that the system does not depend on individual memory.

3.8 Data protection, ethics and safeguarding

·Apply data-minimisation, purpose-limitation and role-based access principles across collection, analysis, reporting and evidence verification.

·Ensure that personal and sensitive information is not collected beyond what is necessary and explicitly approved through a lawful, safeguarded protocol.

·Support appropriate consent, retention, deletion and access-control practices in line with India's Digital Personal Data Protection framework and applicable programme and CMS policies.

·Identify and escalate data-protection, safeguarding or ethical risks before new tools, analyses or reporting practices are operationalised.

4. Reporting lines and role boundaries

The Manager - Data and Insights will report to the Lead, CMS and work as an embedded member of the programme team. The person will participate in in-person meetings in Delhi/Gurgaon and may work from the programme office when required. They will coordinate routinely with implementation partners and CMS specialists in programme design, technology, data engineering, visualisation, research and capacity building.

The role owns the programme-side data and insight cycle - data logic, quality, hands-on analysis, visualisation, narrative, user requirements, testing, adoption and learning. The person is expected to work independently with data and prototype practical outputs, but is not expected to write production software or engineer the core database. Specialist technology and advanced analytics support will remain available, with this role ensuring that such work is grounded in programme needs and fit for use.

5. Qualifications and experience

Essential

·Postgraduate qualification in data analytics, statistics, economics, public policy, management, monitoring and evaluation, social sciences or a related field.

·At least 7 years of relevant experience spanning programme data, analytics, MEL, insights or digital decision-support, preferably within a complex multi-partner or multi-location programme.

·Demonstrated ability to work hands-on with large or messy datasets, trace errors, reconcile sources, build repeatable analyses and maintain close attention to detail.

·Advanced Excel skills and strong working experience with a visualisation platform such as Power BI or Tableau; working knowledge of SQL and familiarity with R, Python, low-code or data-collection tools are strong advantages.

·A strong portfolio of dashboards, visual analyses, insight notes or other work that shows both analytical rigour and good visual judgement.

·Experience translating programme needs into requirements for a digital MIS, participating in user acceptance testing and supporting adoption by non-technical users.

·Ability to turn complex quantitative and qualitative evidence into clear narratives, presentations and decision-oriented conversations for varied audiences.

·Strong interpersonal, facilitation and capacity-building skills, with the confidence to engage programme teams, specialists, partners and leadership.

·Excellent written and spoken English; proficiency in Hindi is strongly needed. Willingness to travel to programme locations as required is essential.

Preferred

·Experience in an embedded data, insights, MEL or programme-management support role within a complex, multi-partner programme.

·Experience collaborating with product managers, developers, designers, data engineers or advanced analytics specialists.

·Exposure to partner performance management, large-scale programme information systems or results-linked programmes.

·Understanding of data protection, research ethics and safeguarding when working with personal or sensitive information.

6. Core competencies

·Data curiosity: enjoys working through the detail, asks what sits behind a number and follows unexpected patterns to their source.

·Attention to detail: catches calculation, labelling, filter and narrative inconsistencies before they reach decision-makers.

·Analytical judgement: distinguishes a genuine programme signal from a data-quality problem and explains uncertainty honestly.

·Visual and narrative thinking: identifies the simplest, most compelling way to communicate a complex pattern without distorting it.

·Technology fluency: learns new tools quickly, understands data and system logic, and communicates effectively with technical teams.

·Creativity: experiments with analyses, formats and approaches while remaining rigorous about evidence.

·Engagement: works comfortably and proactively across programme, partner, technical and leadership teams.

·Execution discipline: follows data issues and agreed actions through to closure and maintains clear documentation.

·Ethical practice: protects confidentiality, applies safeguarding judgement and challenges inappropriate data collection or use.

7. Terms and application

The position is envisaged as a full-time engagement for an initial period of 12 months, extendable based on programme requirements and performance and eventual transition into programme itself. Compensation will be commensurate with experience and CMS policy. The role will be based in Delhi/Gurgaon and will require regular in-person participation in programme meetings, the flexibility to work from the programme office when needed, and occasional travel to other programme locations.

Applicants should submit a curriculum vitae, a brief cover note explaining their fit for the role, and one relevant work sample such as a dashboard, visual analysis, insight note or MEL product. Shortlisted candidates may be asked to complete a practical data-analysis and visual-storytelling exercise and participate in a structured interview. CMS is an equal-opportunity employer committed to an inclusive workplace.