Summary:

- Role type : Contractor
- Engagement window: Full-time during the base-brain build (Aug-Dec) fractional ongoing (Jan-Apr)
- Location: Tier-1 India (Bangalore • Hyderabad • Chennai • Pune • Mumbai • Delhi NCR); remote-friendly
- Reports to : CTO
- Team : Likely a lead Data Analyst + AI, scaling to a small pool for the global expansion

About the role

The Data Analyst is the primary resource who marshals the documentation that builds the platform's base Brain. The Brain produces a credible screening PCF by ontological equivalence estimating a supplier's missing data from comparable suppliers (same material process country). That only works once a reference base (~1,500 suppliers) populates the equivalence space. Building that base is what makes meaningful output possible for any customer so this role is foundational and on the critical path.

The platform serves automotive OEMs and their Tier-1 suppliers, whose supplier base is global so the base you build is global and multi-language, not India-only. You will not do this alone or by hand: you work AI-first (Claude to extract, translate and structure across languages) on the pipeline the AI/ML Platform Engineer builds, and the base is built coverage-first EU + India (the high-yield, high-relevance regions) first for general audience, then China / SE Asia / Americas as expansion. You run the ingestion; you do not build it.

Key responsibilities

Source & marshal the documentation (global)

- Global sources EU company registers + CSRD-cascade disclosures, national registries, OEM / Tier-1 supplier portals, India (BRSR / MCA), Japan / Korea filings, industry associations and global databases per the approved-source registry.




- Multi-language, AI-first — use Claude to extract, translate and structure non-English sources at scale; you curate and QA, the AI does the volume.
- IP discipline — ingest derived attributes only; never redistribute source documents; every field carries a source and confidence tier (A/B/C).

Build the base — coverage-first

- EU + India first — build coverage of the equivalence neighbourhoods (material process country) for OEM/Tier-1 suppliers in the high-yield regions first, so the platform can ride on a covered sub-footprint.
- Default-anchor the hard regions — where public data is sparse (China, SE Asia, N. Africa), anchor with regional / sector defaults + a few representative suppliers rather than fully sourcing each; improve as primary data arrives.
- Structure & resolve — normalize into the L2 schema; resolve entity-resolution and source-format exceptions; one canonical profile per supplier.

Quality, provenance & drift

- QA against the golden set — check profiles within tolerance; triage and flag gap-fills; keep provenance + tier on every field.
- Monitor drift — watch source-format changes across regions; keep the source registry current.

What success looks like — coverage-driven phasing

- Foundation (10) — taxonomy, ingest contract,



entity-ID and gates frozen; per-region automated yield measured; golden set seeded.
- 50 — cohort model proven; EU + India yield confirmed on real cohorts.
- ~150 — EU + India core equivalence neighborhoods started; first meaningful screening outputs.
- ~500 (GA-ready) — EU + India covered credible screening PCFs for EU + India suppliers.
- GA-usefulness threshold.
- 800 1,500 (post-GA) — add China / SE Asia / Americas — multi-language, default-anchored where data is sparse.

Required qualifications

- Data literacy — spreadsheets and SQL; some Python a plus; can structure messy source data into a defined schema.
- Research & sourcing skill across jurisdictions — finding and extracting the right data from reports, filings and portals; robust attention to detail.
- AI-first working — fluent using Claude and similar to extract, translate and structure at scale, with human judgement on exceptions and QA.
- Comfort with multi-language sources (via AI translation); rigour on provenance (source • date • confidence tier).
- Willingness to learn the carbon / LCA and automotive supply-chain domain; 2–4 years in a data / research / analyst / operations role; volume data collection & QA a strong plus.

Boundaries

- Owns — sourcing, structuring and QA of the global base-Brain data.
- Not this role — building the ingestion pipeline or L4 (AI/ML Platform Engineer); taxonomy / methodology governance (CTO); the programme (PM); the customer-facing product (Product Engineer).
- Seam — operates the pipeline the AI/ML Platform Engineer builds; escalates taxonomy questions to the CTO

📌 Data Analyst (Bengaluru)
🏢 Prohash Technologies
📍 Bengaluru