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Pranav Deo Staff Product Manager at Rakuten

Ads. AI. Marketplaces.Built around people.

I connect advertiser goals, consumer needs, and the technical systems that turn product decisions into real outcomes.

$500M+Annual revenue reached by Programmatic Loyalty, the platform I launched at Rakuten.
Explore my work 8 consumer prototypes
Sunnyvale, CaliforniaRésumé (PDF) ↗
Technical depth

Product ownership across recommendations, AI workflows, and ads infrastructure.

32% lift

New-buyer acquisition through Personalized Rewards, validated with randomized experiments.

30% fewer

Creative revision cycles through multi-agent GenAI orchestration at Rakuten.

01 / Building at scale

Advertiser goals.
Customer value. Shared systems.

At Rakuten, I connect campaign economics and customer incentives with recommendations, experimentation, and AI. These two stories show the product ownership and technical decisions behind the outcomes.

01 / Selected workProfessional work

Product flow

  1. 01Advertiser spend + target ROAS
  2. 02Commission, pacing + incentive decisions
  3. 03Campaign and customer outcomes

Ads, marketplaces & recommendations

Programmatic Loyalty: aligning advertiser goals and customer incentives.

Launching Programmatic Loyalty: a recommendation and optimization platform that translates advertiser goals into commission rates, budget pacing, and customer incentives.

Product leadership across marketplace economics, recommendations, and real-time data.

Read the story : Programmatic Loyalty: aligning advertiser goals and customer incentives.
02 / Selected workProfessional work

Product flow

  1. 01Shared campaign + marketplace signals
  2. 02Recommendation, ranking + pacing
  3. 03Common APIs + integration contracts

AI platforms & infrastructure

Better creative workflows. Shared decisions. Infrastructure that keeps up.

At Rakuten, I lead product work across shared decisioning, multi-agent creative workflows, and the infrastructure that supports them.

Connecting AI orchestration, shared platform contracts, and operational performance.

Read the story : Better creative workflows. Shared decisions. Infrastructure that keeps up.

Ads & marketplace economics

Align the incentives.

From revenue-per-click and cost-per-click modeling at Media.net to ROAS-driven recommendations at Rakuten: connect advertiser returns, budget pacing, and customer rewards.

Measurement & growth

Separate signal from noise.

Randomized experiments with CUPED, cohort targeting, and propensity models. My acquisition work spans a 32% new-buyer lift and $16M in incremental GMV across separate initiatives.

AI & infrastructure

Make the experience dependable.

Define orchestration, evaluation, APIs, and data requirements with engineering and data science. Balance workflow quality, recommendation speed, and operating cost.

02 / Getting closer to the user

Big systems start
with everyday friction.

The same product questions show up in a crowded inbox, a canceled order, or a group trying to meet. I build independent prototypes to explore the experience directly—and make the tradeoffs tangible.

08 independent explorations

Open a prototype to try the interaction. Read its story for the product bet, technical approach, and what is simulated.

Personal projects, independent of the products and companies referenced. Prototype interactions demonstrate concepts; they are not shipped features or measured business outcomes.

03 / The experience behind the decisions

From ad economics
to product leadership.

Media.net grounded me in advertising economics. LeanData brought workflow product ownership. At Rakuten, that scope grew across acquisition, marketplace optimization, AI, and infrastructure.

Rakuten

San Mateo, CA

May 2021 – Present

Staff Product Manager

Jun 2025 – Present

AI platforms & ads infrastructure

Product architecture for multi-agent GenAI workflows, shared recommendation and ranking systems, and cloud infrastructure. Partnering with engineering and data science on the contracts and metrics that make platforms reusable.

20% faster recommendation response · 30% fewer creative revision cycles

Senior Product Manager

Feb 2022 – Jun 2025

Recommendations & optimization

Launched Programmatic Loyalty and defined personalization, cohort targeting, and online/offline decisioning across Kafka, Redis, and Snowflake.

Platform scaled to $500M+ annual revenue · Contributed to 20% conversion lift

Product Manager

May 2021 – Feb 2022

Ads performance & experimentation

Launched Personalized Rewards, designed randomized experiments with CUPED, and partnered with data science on propensity modeling for merchants with sparse historical data.

32% lift in new-buyer acquisition · $16M incremental GMV

LeanData

Santa Clara, CA

Aug 2020 – May 2021

Product Manager

Aug 2020 – May 2021

Workflow automation & decisioning

Shipped no-code rules and workflow orchestration. Built real-time lead routing and led the integration of predictive scoring into production CRM workflows.

40% fewer configuration escalations · 21% higher conversion through predictive scoring

Media.net

Mumbai, India

Aug 2017 – Jul 2019

Product Analyst

Aug 2017 – Jul 2019

ML optimization & platform analytics

Built Python regression models to connect campaign performance with traffic economics and translate analysis into bidding and traffic decisions.

Contributed to a 20% increase in ad revenue

04 / Perspective

A quantitative foundation.
A builder’s curiosity.

I started on the data side at Media.net in Mumbai. A master’s in Statistics at the University of Maryland deepened how I think about uncertainty, experiments, and the difference between a signal and a useful decision.

Since then, I’ve worked on revenue workflows at LeanData and advertising, recommendation, AI, and infrastructure platforms at Rakuten. I like problems where the customer experience depends on getting the underlying system right.

I also build independent prototypes to make product questions concrete. Some explore a live AI pipeline; others deliberately simulate the model so I can focus on the interaction. Each story makes that distinction clear.

Away from the roadmap: my reading shelf ↗

05 / What comes next

Let’s build
something that matters.

I’m interested in product leadership where ads, marketplaces, AI, and consumer experiences meet—and where technical depth and product judgment need to work together.

Based in Sunnyvale, CA.
Open to remote, hybrid, and relocation.

deopranav2808.work@gmail.com
LinkedIn ↗Résumé (PDF) ↗

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