Reports to: CEO
Location: Turbhe, Navi Mumbai
Mandate horizon: IPO readiness and the 3-year turnover-doubling trajectory
Why this role exists now
Vashi runs on capable platforms — S/4HANA, Salesforce, e-commerce, Digiserve — and a technology team that keeps them reliable. That foundation is solid and must stay solid. But the next phase of growth will not be won by running systems better. It will be won by making Vashi faster and easier to do business with, by taking time, cost and friction out of how the business actually runs, and by making AI part of everyday work before the market forces the question.
Today, technology leadership is organised around running the estate: infrastructure, platforms and applications. What is missing is one accountable owner for the change agenda — the person who decides which workflows to transform and how, builds the business case, takes AI out of pilots and onto the floor, and proves the result in turnaround time, customer experience and gross profit. This role exists to be that owner.
We are not hiring a platform steward, a programme administrator or an innovation lab. We are hiring a leader who treats every quote, every procurement cycle, every credit decision and every delivery as a process that can be made dramatically faster, cheaper and more trustworthy — and who gets the change adopted.
This is the go-to leader for AI and digital change at Vashi — a builder of velocity and adoption, not a custodian of systems.
The mandate
• Velocity: Cut the time Vashi takes to respond to the market — quotation, order processing, credit, procurement, fulfilment — using AI and automation, with results visible in turnaround time (TAT), not slideware.
• Customer: Make Vashi measurably easier and faster to buy from, so that customers feel the speed and the LGP relationship deepens. A tool that does not reduce friction or build trust is not serving our purpose.
• Productivity & profit: Raise productivity and lower cost-to-serve across the cluster model, with every initiative carrying a business case that Finance validates in gross-profit and working-capital terms.
• AI-first: Make AI and data science a standing capability across the company — use-case pipeline, models in production, guardrails, adoption and measurement — raising the Digital Quotient of every function. Pilots and models that never reach the floor do not count.
• Make it stick: Lead adoption through the business heads and the technology peer group, so that new ways of working are used every day, not demonstrated once.
What you will own
Transformation portfolio & value
• Own a prioritised map of Vashi’s end-to-end workflows — quote-to-cash, procurement,
credit, fulfilment — and a hard target for cycle-time and cost reduction in each, with a
baseline measured before work begins.
• Own the business case and benefits tracking for every transformation and AI initiative —
one scoreboard, validated by Finance, covering TAT, customer experience, productivity
and gross profit.
• Drive build-vs-buy-vs-partner decisions with frugality: every rupee must earn its keep. No
over-engineering, no vanity pilots, no consultant decks without a deployed result.
AI capability
• Stand up the AI operating model: use-case pipeline, data readiness, guardrails and
responsible use (including data-privacy obligations), adoption and measurement across
functions.
• Move AI from pilots to production — embedded in how sales, procurement, credit, support
and finance actually work day to day — and retire the pilots that do not earn their place.
• Be the in-house authority on what AI can and cannot do for Vashi, separating real
capability from vendor hype for the CEO and the leadership team.
Data science & decision intelligence
• Own the data science agenda for the workflows that matter most — for example demand
forecasting and replenishment, quote and margin optimisation, credit-risk scoring,
procurement analytics and customer propensity — chosen by value, not by novelty.
• Build a lean data science and ML capability and run models as products: every model has
an owner, the decision it changes, a measured value, and a plan for monitoring, retraining
and retirement.
• Define decision-grade data with the business and the peer group. Platforms and
Infrastructure own the data platform and pipelines; this role owns the models, the use
cases and the decisions they improve.
• Set model governance — validation, explainability, drift and bias checks, and data-privacy
compliance — so that managers can trust models and auditors can stand behind them.
Customer experience & speed to market
• Redesign the customer-facing journeys — quote to order, order status, credit, delivery and
service — so that speed and transparency are built in, not added by effort.
• Make speed a system, not an effort: faster quotation, lower TAT and fewer manual
handoffs across the cluster model.
Working through the technology peer group
• Set the transformation and AI direction. The Heads of Infrastructure, Platforms and
Applications own run, reliability, security (including the live MDR / cybersecurity-audit
decisions) and platform health of the S/4HANA, Salesforce, e-commerce and Digiserve
estate. Agree one shared roadmap and clear decision rights so that change lands on stable
ground.
• Give peers a bigger game: AI-ready data, integrations and platforms that they build and
own, while this role stays accountable for the business outcome.
Leadership & governance
• Build a lean, senior core team (product, AI, automation and analytics) and a network of
digital champions in the business — drawing first on capability Vashi already has, and
adding only the few hires the agenda needs.
• Hold vendors and partners to outcomes, not effort.
• Translate technology into the language of the board, the P&L and the equity story — and
back into priorities the floor can execute.
What success looks like in the first 12 months
• A clear, costed transformation and AI roadmap tied to TAT, customer experience and gross
profit — built with the technology peer group and agreed with the CEO inside the first 90
days, with a baseline and a target for every priority workflow.
• At least two core workflows measurably faster and cheaper, with the change adopted and
sticking, not just demonstrated.
• AI in live production use in more than one function, with adoption and impact that can be
measured and that Finance trusts.
• At least one data science model — for example demand, pricing, credit or replenishment —
in live use and changing a real decision, with value measured in margin, working capital or
TAT.
• At least one customer-facing journey visibly faster or simpler, with evidence from
customers or the LGP network.
• A working model with the Heads of Infrastructure, Platforms and Applications in which
decision rights are clear and disagreements are resolved without escalation.
• AI guardrails and responsible-use rules approved and operating, so that adoption never
creates a risk the board or auditors have to explain.
Who we are looking for
The right person is a hands-on, business-first transformation leader who has taken AI and digital
change into the daily work of a real, physical, margin-sensitive business — not a programme
manager who governs other people’s delivery, and not a pure technologist or data scientist with
no feel for how a quote or a credit decision is actually made.
• Track record: Has personally driven workflow, cycle-time and customer-experience
transformation in a customer-facing, product-based, service-led business — and can point
to time taken out and money saved.
• AI and data native: Has shipped AI and data science models into production and changed
how work is done, with real users and measured adoption. Hands-on enough to tell a
working capability from a demo, and a vendor claim from a result.
• Commercial: Speaks gross profit, working capital, cost-to-serve, ROI and IPO readiness
fluently. Technology is a means to a commercial end, never the end itself.
• Frugal: Frugal by instinct — builds for return, not for headcount or prestige. Has proved
value with a lean team before asking to scale.
• Influencer and multiplier: Gets change adopted through peers and business heads who
do not report to them, and makes the people around them more capable — not a hero who
hoards the work.
• Instills trust: High ownership; does what they say; surfaces bad news early. This is
Vashi’s most important competency.
Profile specifications
The background we expect, so that candidates and recruiters can screen quickly:
Specification What we expect
Level & experience Vice President (L5). 15+ years of experience, including at least 5
years leading AI, data science or digital transformation with
measured business outcomes.
Industry A customer-facing, product-based, service-led business — one that
sells products and wins on speed, availability and service quality
(for example B2B distribution, industrial and engineering products,
automotive and aftermarket, or retail and e-commerce). Puresoftware,
IT-services and back-office IT backgrounds are not a fit
on their own.
Organisation size Has worked in an organization with ₹ 6000 Cr – 8000 Cr+ annual
turnover and 3,000+ people, operating across multiple locations
or regions. Larger groups are welcome where the candidate has
worked lean.
Budget managed Has owned — not just influenced — an annual technology, digital
or AI budget in crores, accountable for return, vendor negotiation
and build-vs-buy decisions.
Team managed Has led a multi-disciplinary team of roughly 20 – 25 people (direct
and indirect, across data science, engineering, product and
analytics). At Vashi the core team will start lean.
