VP Data Science · AI & Data Science · Bengaluru · India
VP Data Science AI & Data Science Recruitment
Bengaluru
60+ AI & Data Leadership Placements — typical mandates close in 105-130 days, with a 12-month candidate guarantee.
Specialisation withinTechnology & Digital·AI & Data Science·Bengaluru, Karnataka
A VP Data Science mandate at a Bengaluru-anchored AI-and-data-science platform is a multi-year data-science-and-ML-engineering-org scaling, applied-data-science-and-ML-platform stewardship and data-science-and-ML-engineering-talent-acquisition-and-retention discipline seat before it is a P&L seat. The successful candidate owns the multi-year data-science-and-ML-engineering-org architecture, governs the applied-data-science-and-ML-platform compounding cycle (feature stores, model training, model serving, MLOps and applied-data-science delivery), holds the data-science-and-ML-engineering-talent-acquisition-and-retention discipline (the binding constraint), and reads the multi-stakeholder operating cadence CTO, Head of AI, CEO and venture-and-strategic-capital sponsor-board together require.
The VP Data Science Seat in AI & Data Science, Bengaluru
VP Data Science mandates at Bengaluru AI-and-data-science platforms are structurally the cost-efficient leadership-recruitment tier. Indian top-tier executive-search firms typically don't pursue this tier; specialist leadership-recruitment firms running 12-15% retainer architecture cover the bench. The Bengaluru AI-research-and-applied-data-science talent base, the venture-and-strategic-capital-backed AI-platform cohort and the deep India ML-engineering talent pool together shape the bench architecture.
We over-index on operators who have led a Tier-1 AI-and-data-science platform data-science-and-ML-engineering-org through a sustained multi-year scaling cycle, navigated an applied-data-science-and-ML-platform compounding cycle as the accountable data-science leader, or held credible CTO, Head of AI, CEO and venture-and-strategic-capital sponsor-board dialogue alongside data-science-and-ML-engineering-org governance.
Why Bengaluru for AI & Data Science Leadership
Bengaluru is India's AI-and-data-science data-science-and-ML-engineering-org capital. The deepest Indian AI-research-and-applied-data-science talent base, the densest concentration of venture-and-strategic-capital-backed AI-platform data-science-and-ML-engineering orgs, the largest pool of data-science-and-ML-engineering talent and the most-developed applied-data-science-and-ML-platform ecosystem all anchor in the city.
Vice President of Data Science Profile — AI & Data Science in Bengaluru
Bengaluru AI-and-data-science VP Data Science candidates typically come from one of three benches: prior VP Data Science or Head of Data Science tenure at a Tier-1 venture-or-PE-backed AI-or-data-science platform, prior senior data-science-leadership tenure at a global AI-or-data-science platform with subsequent India-VP Data Science crossover, or prior India-Principal-Data-Scientist-or-Distinguished-Data-Scientist tenure at a Tier-1 AI-or-data-science platform with subsequent VP Data Science crossover. The seat requires multi-year data-science-and-ML-engineering-org-scaling credibility, applied-data-science-and-ML-platform compounding discipline, data-science-and-ML-engineering-talent-acquisition-and-retention architecture (the binding constraint) and the venture-and-strategic-capital sponsor-board governance rhythm.
Compensation Benchmark
Tier-1 Bengaluru AI-and-data-science VP Data Science packages typically land ₹1.8-4.5 crore fixed cash for venture-or-PE-backed-platform VPs of Data Science, 30-60% short-term incentive tied to data-science-and-ML-engineering-org-scaling, applied-data-science-and-ML-platform KPIs and data-science-talent-retention metrics, plus material ESOP / RSU vesting tied to venture-and-strategic-capital fundraising and (where applicable) US-listing or pre-IPO progression. Foreign-OEM India AI-and-data-science VP Data Science equivalents command ₹3-8 crore fixed (frequently dollar-denominated with RSU vesting on global parent stock).
Key Leadership Challenges in AI & Data Science
Inherited from the AI & Data Science parent practice. Each challenge calibrates differently for a VP Data Science mandate in Bengaluru.
Hiring a Chief AI Officer or Head of AI who can bridge research credibility, applied product delivery, and board-level AI governance — a combination that is rare in any single candidate.
Building an AI platform team — training infra, serving infrastructure, feature stores, MLOps, evaluation frameworks — that can support multiple product lines without becoming a bottleneck.
Responsible AI leadership — Heads of Model Risk, AI Governance, and AI Ethics who can operate under DPDP, GDPR, and emerging EU AI Act frameworks while not blocking commercial velocity.
Chief Data Officer hires — CDOs who can unify data contracts, governance, and a single logical data plane while enabling self-serve analytics and AI-ready data products.
Senior individual contributor hiring — principal research scientists, distinguished AI engineers, and applied research leads where a single hire can shift technical trajectory.
GCC AI leadership — site leads for global AI platform teams who can operate as dual-reporting leaders to global AI heads and Indian country MDs.
Candidate Archetypes for VP Data Science AI & Data Science
The Chief AI Officer
Leader who combines research credibility (typically a PhD from a top-tier institution and published work or production LLM deployments) with applied delivery experience. Fluent in board-level AI governance and commercially bilingual with product and engineering organisations.
The Applied AI VP
Operator who has shipped production fine-tuned, RAG-based, or agentic AI systems at scale. Deep understanding of evaluation, guardrails, prompt engineering at system level, and the MLOps architecture that makes applied AI reliable.
The ML Platform Leader
Infrastructure engineer who has built training and serving infra for foundation-model or high-volume applied-AI workloads. Fluent in GPU scheduling, distributed training, inference optimisation, and the feature-store / model-registry stack.
The Chief Data Officer
Data leader who has rebuilt an enterprise data platform for AI-ready consumption — data contracts, governance, single logical data plane, and self-serve analytics. Often has operated inside BFSI, consumer, or healthcare contexts where data governance is a board-reported topic.
The Principal Research Scientist
Individual contributor with published work in top-tier venues (NeurIPS, ICML, ICLR) or demonstrable contributions to widely-used open-source foundation models. Often a PhD from a top-tier ML research group, with subsequent production experience at a foundation-model lab.
The AI Governance Leader
Risk, compliance, or legal leader who has built a model-risk or AI-governance function. Operates under DPDP, GDPR, and EU AI Act frameworks; understands the intersection of model validation, disclosure, and board-committee governance.
Frequently Asked — VP Data Science AI & Data Science Mandates in Bengaluru
Which recruitment firm should I partner with to hire a VP Data Science for my Bengaluru AI-platform?
Leadership-recruitment firms running 12-15% retainer architecture cover the Bengaluru AI-and-data-science VP Data Science bench. Tier-1 Indian executive-search firms typically focus on C-suite mandates and don't pursue the VP-and-Director-tier. We run a research-driven slate-building approach for VP Data Science mandates with a 60-90 day calibration-to-offer cycle.
How long does a retained VP Data Science search for a Bengaluru AI-and-data-science platform typically run?
60-100 days from calibration memo to signed offer. Pre-IPO and pre-exit platforms add 2-3 weeks at the back end for venture-and-strategic-capital board reference work; AI-research-anchored platforms add a similar window for data-science-research-publication-and-recognition reference cycles.
What multi-year data-science-and-ML-engineering-org-scaling and applied-data-science-and-ML-platform exposure should a Bengaluru AI VP Data Science slate carry?
Direct ownership of a Tier-1 AI-and-data-science platform data-science-and-ML-engineering-org through at least one multi-year scaling cycle, paired with applied-data-science-and-ML-platform compounding credibility and data-science-and-ML-engineering-talent-acquisition-and-retention architecture (the binding constraint). Operators without multi-year data-science-and-ML-engineering-org-scaling and applied-data-science scar tissue rarely clear the second calibration round.
Are returning-NRI candidates viable for Bengaluru AI-and-data-science VP Data Science mandates?
Materially viable for operators with prior global-AI-and-data-science-platform data-science-leadership tenure or peer-international AI-and-data-science VP Data Science experience. Prior data-science-research-publication-and-recognition credibility is a parallel consideration for AI-research-anchored platforms. The Mumbai–Bengaluru capital-markets corridor and the US-and-Europe-anchored AI-and-data-science platform ecosystem onboard returning-NRI VP Data Science through global-AI-and-data-science platform comparators with relative ease.
Adjacent Roles We Place in AI & Data Science
Regulatory & Compensation Context — AI & Data Science
Regulatory Backdrop
AI leadership is hired into an intensifying regulatory envelope. India's DPDP Act has introduced data fiduciary obligations, consent architecture, and cross-border transfer restrictions that shape both ML training data practices and inference pipelines. The RBI's Model Risk Management Guidelines (MRMG) require model inventory, independent validation, and ongoing performance monitoring for any AI used in credit, fraud, or consumer-facing financial decisions. SEBI and IRDAI have both issued consultation papers on AI/ML in capital markets and insurance — not yet fully binding but shaping governance expectations. Globally, the EU AI Act's risk-tier classification, GDPR's Article 22 on automated decision-making, and US state-level AI regulation (New York City Local Law 144, Colorado's AI Act) materially affect leadership candidates hired into India-based roles that serve global products. Responsible AI committees, model-risk registries, and independent AI audits are now standing board-committee topics at leading enterprises. Candidates for senior AI roles are increasingly evaluated on their ability to operate under these frameworks, not just their technical depth.
Compensation Architecture
AI leadership compensation sits at the top end of the technology market. A Chief AI Officer at a well-capitalised AI-first SaaS franchise or a large enterprise commands ₹6-15 crore fixed cash with 1-3% equity and meaningful bonus opportunity tied to technical and product milestones. VPs of Applied AI and ML Engineering price at ₹4-8 crore fixed with 0.5-1.5% equity. Principal research scientists and distinguished engineers — the IC roles — have the most stretched compensation market: US-benchmarked USD cash packages of $400K-$800K plus equity, dual-jurisdiction structures, and signing bonuses that can match fixed cash. CDOs price at ₹3-6 crore fixed. AI Governance and Model Risk leaders, particularly in BFSI, sit at ₹3-5 crore fixed with strong cash bonuses. For GCC AI site leads, comp tracks the global parent's AI leadership band (typically USD-anchored) rather than Indian country-MD bands. Retention is a first-class problem — counteroffers from foundation-model labs and global hyperscalers are now standard on every senior AI exit; we advise clients on retention architecture (refreshers, secondaries, confidential scope expansions) alongside the initial hire.
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