P.V.Raj Vineeth | Profound

P.V.Raj's Profound AI Rep

His stories run from becoming the sole Hadoop lead at CGI to doubling daily active users on a global trade intelligence platform at Trademo.

P.V.Raj Vineeth

Open to meet

OPEN TO

Founders Running Lean Product Teams

Early to mid-stage founders operating with a small or solo PM function.

OPEN TO

B2B SaaS PMs Working on Engagement

Product managers at B2B SaaS companies working on user engagement and retention problems.

OPEN TO

Operators Building Data Products for Enterprise Clients

PMs, consultants, or founders building data platforms or analytics products for enterprise buyers.

OPEN TO

Engineers and PMs Integrating AI into Data Products

Engineers or product managers working on integrating AI features into existing data products.

P.V.Raj Vineeth

Edges

Owning Full-Cycle Product Delivery

Raj owns the complete product thread from customer discovery to post-launch iteration, without handing off at any stage. He interviews customers, writes PRDs, manages sprint boards, coordinates engineering, and gives product demos to customers and sales prospects. This pattern has held across Trademo Intel, Trademo MAP, and Nielsen, in each case as the sole PM responsible for the full lifecycle. The mechanism is direct access: he talks to customers, sales, and customer success before writing a single requirement, then stays in the loop through engineering and into the market. For teams that need a PM who can cover the full surface without a supporting cast, this is the operating mode he defaults to.

Spotted in 3 Stories

Building Persona-Driven Engagement Systems

Raj's approach to engagement problems starts with understanding who the user is and what they need to see immediately, then building a curated experience around that. He does not optimize engagement in the abstract; he maps it to specific personas and their workflows. This pattern produced Intel Hub and MAP Hub at Trademo, both built on the same insight: users were leaving because the platform was not surfacing what was relevant to them on arrival. The mechanism is persona segmentation before solution design: he identifies the dominant user types, maps their core jobs-to-be-done, and builds the experience around reducing friction to the most relevant data. The result is an engagement model that is repeatable across product surfaces, not tied to a single feature or platform.

Spotted in 2 Stories

Shipping Incrementally Under Constraint

When Raj faces a blocker, whether technical, organizational, or compliance-driven, his instinct is to find the smallest version that proves the direction and ship that first. He does not wait for the full solution when a partial one can build confidence and momentum. This pattern appears across three distinct contexts: proposing a beta for contested AI features at Trademo, launching Ask Nielsen with a reduced data set at Nielsen, and phasing the cloud migration at Tata by starting with non-PII data to build client trust. The mechanism is consistent: identify the constraint, find the minimum viable version that still delivers value, ship it, and use the result to unlock the next step. For teams that need a PM who can keep things moving without waiting for perfect conditions, this is how he operates under pressure.

Spotted in 3 Stories

P.V.Raj Vineeth

Career Stories

Trademo Intel Hub: Doubling Engagement with Persona-Driven Dashboards

At Nielsen as Senior Product Manager

2024

Ask Nielsen: Shipping an AI Product Under Data Privacy Constraints

At Nielsen as Senior Product Manager

2024

Trademo MAP Hub: Extending the Engagement Model to the Second Platform

At Nielsen as Senior Product Manager

2024

Tata Insights: Delivering a Data Governance Platform for an Enterprise Client

At Tata Insights and Quants - A Division of Tata Industries Limited as Product Manager - Analytics Services

2022 - 2024

CGI Hadoop Lead: From Fresher to Sole India-Side Delivery Owner

At CGI as Software Engineer

2019 - 2021

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