Talent Researcher

About The Convergence Foundation

The Convergence Foundation (TCF) is an Indian philanthropic foundation dedicated to catalysing rapid and sustained economic growth to enhance the lives of all Indians. Established by Ashish and Manisha Dhawan in April 2021, TCF builds on their legacy of impactful philanthropy, including the founding of transformative institutions like Ashoka University and the Central Square Foundation.

Our mission is clear: to transform the lives of all Indians through rapid, sustained, and inclusive economic growth. To achieve this, we focus on seven key program areas that influence job creation, human capital development through education and skilling, and strengthening state capacity to deliver outcomes.

In each area, TCF works on:

• Building pioneering institutions to address India's most complex socio-economic challenges.
• Shaping the larger ecosystem and sharing knowledge, insights and learning with other philanthropists, governments and key stakeholders

We believe that the government is the key actor for system change, and the role of philanthropy is to strategically support the government.

The TCF Network includes 16+ organisations, each committed to addressing specific areas of India's socio-economic development, from school education and governance to women's economic empowerment and export competitiveness.

Role purpose

Build clean, useful talent intelligence so recruiters can close faster. You’ll map markets and organisations, produce high-quality longlists, keep Manatal data tight, and share simple insights on where to hunt next for TCF and partner organisations.

What you’ll do

• Turn role → search system:

  Convert intake into must-haves, adjacent titles, exclusion terms, and a reusable search grammar (Boolean/X-ray strings, title/skill taxonomies).

• Market sizing:

  Estimate pool sizes by city/company/seniority; call trade-offs early (e.g., role–location–comp constraints).

• Org deconstruction:

  Map target companies/teams, feeder functions, and look-alikes (incl. GCCs, Tier-2 cities); surface hidden pockets.

• Targeting strategy:

  Build a tiered

  A/B/C target company list

  with reasons; maintain a living watchlist of additions/drops.

• Longlist production:

  Ship 60–100 validated profiles per role with notes on fit signals; remove duplicates; tag properly in Manatal.

• Diversity slates:

  Ensure agreed representation in every slate; show viable alternates if supply is thin.

• Comp & location scan:

  Provide directional comp bands and city differentials from public data + our history; flag red lines early.

• Signal tracking:

  Monitor hiring freezes, funding rounds, org changes; refresh priors weekly.

• Outreach accelerators:

  Hand recruiters suggested angles and short bullets per segment (what to pitch, likely hooks); A/B subject ideas when useful.

• Data quality:

  Own tags, taxonomies, and a simple data dictionary in Manatal; keep an audit trail.

• Reporting:

  Weekly 1-pager per role—coverage %, list quality, risks/blockers, next bets. Maintain a role dashboard in Sheets.

What good looks like (first 90 days)

• Day 5:

  First longlist + A/B/C target list per role.

• Day 30:

  Org maps for top 5 companies per role family; diversity slate pattern in place.

• Day 60:

  Hit ≥80% coverage of agreed targets; comp/location notes standardised.

• Day 90:

  Stable weekly research notes; duplicate rate near zero; recruiters using your search grammar by default.

KPIs

• Time to first slate:

  ≤ 5 working days from intake.

• Longlist quality:

  ≥ 70% longlist → recruiter-accepted; ≤ 5% email bounce rate (where emails are sourced).

• Coverage:

  ≥ 80% of agreed target companies covered within 3 weeks.

• Conversion:

  ≥ 30% longlist → recruiter outreach; ≥ 15% outreach → screening call.

• Data hygiene:

  100% records tagged; 0 stale trackers; privacy checklist met.

You’ll fit if you

• Have

  3–6 years

  in any

  ops/research/analyst

  style role (KPO, market research, MIS/PMO, consulting analyst).
• Enjoy structured digging and clean spreadsheets more than “spray and pray.”
• Are strong with Excel/Sheets (filters, pivots, XLOOKUP/VLOOKUP, COUNTIFS; regex is a plus).
• Are a power user of LinkedIn search; quick to learn Manatal conventions.
• Write tight notes; handle multiple roles without dropping details.
• Like owning a system end-to-end and being judged on clarity and speed.

Nice to have

• Talent Insights or any labour-market intel tool experience.
• Light automation for data cleaning (Sheets formulas/App Script or basic Python).
• Familiarity with research/policy/consulting talent pools / environment.