Research-driven product candidate combining product strategy, business analysis, structured discovery, and applied research to turn ambiguous problems into clear product decisions.
Double Master's graduate across Italy and Poland, with research in product discovery and practical exposure to market analysis, platform analysis, stakeholder coordination, and requirements validation. Interested in contributing to international product teams where thoughtful discovery leads to measurable outcomes.
Yash's path runs through two Master's programmes — International Economics & Commerce at UNIVPM in Ancona, Italy, and International Management at Gdańsk University of Technology, Poland — a route that forced him to keep re-explaining ambiguous problems to new audiences, in new languages, from scratch. That habit became the throughline of his product thinking: before proposing anything, frame the problem so precisely that the solution feels almost obvious.
His Master's thesis pushed that habit into research: a mixed-methods study of how Product Managers actually adopt Generative AI in discovery work, built on an 82-respondent practitioner survey and four enterprise case studies. It produced an original 5-stage adoption maturity model and a framing — the Discernment Principle — that treats AI adoption as a question of calibrated trust rather than raw usage.
Outside the thesis, that same structured instinct shows up in his work across consulting, platform-issue analysis, and eCommerce and travel operations — reading recurring error patterns, coordinating with UK-based stakeholders, and turning findings into requirements a delivery team could act on. The Notion Product Teardown, built independently under hackathon time pressure and placing 4th of 13 teams, is the clearest proof point: a full activation-flow critique, from flow-mapping through falsifiable experiment design, completed in days rather than weeks.
He's now looking for Product Manager, Product Owner, and Business Analyst roles on international product teams — ideally ones where discovery is taken as seriously as delivery.
Able to structure ambiguous problems, identify user and business needs, compare opportunities, and translate insight into product decisions.
Connecting product opportunities to business objectives, evaluating trade-offs, and organizing initiatives around impact and feasibility.
Analyzing platform issues, requirements, recurring errors, operational patterns, and business impact.
Research focus on how Product Managers adopt GenAI, where it creates value, and why human judgement, context, and governance remain essential.
Translating analysis into clear communication for cross-functional and international stakeholders.
KPI thinking, reporting, structured analysis, SQL, Power BI, Google Analytics, and evidence-based prioritization.
How Product Managers are integrating GenAI into discovery work — and what that reveals about trust, not just usage.
How are Product Managers integrating Generative AI into product discovery, and what are the perceived impacts on efficiency, output quality, trust, and role evolution?
A convergent mixed-methods approach: an 82-respondent PM/PO survey combining descriptive statistics with open-text thematic analysis, triangulated against four enterprise case studies (Figma, Notion, Miro, Atlassian) for organisational-level evidence.
Teams advance not by using AI more, but by using it with greater discernment, calibrated trust, and governance maturity.
93% of PMs use GenAI daily or several times a week across discovery work.
Hallucination, missing product context, and privacy/policy limits — not usability — are the binding constraints.
Manual production shifts toward judgement and orchestration — the "strategic editor."
"The advantage no longer belongs to the teams that use AI the most — but to those who have learned, precisely, when not to trust it."
This exploratory study is based on a convenience sample. Findings should be read as directional practitioner evidence rather than statistical generalization to the full Product Manager population.
One a forward-looking Product Owner concept, one a rapid independent teardown — both structured the same way: frame the problem, ground it in evidence, propose falsifiable next steps.
A Product Owner case study for Revolut focused on pre-trade protection for CFDs, crypto, and precious metals, designed on top of Revolut's existing AI stack.
Independent concept case study, not a shipped Revolut product and not evidence of Revolut employment. Built to demonstrate product thinking against a real, publicly documented platform context.
An independent teardown of Notion's sign-up-to-first-workspace activation flow, examining where AI-first defaults, output-quality gaps, and stacked friction points put user activation at risk.
Independent coursework/hackathon analysis, not an official Notion document. Inferred figures are labeled by confidence level (High / Medium / Low).
This portfolio is designed as a living product portfolio. Additional Product Manager, Product Owner, and Business Analyst case studies will be added over time, with each project presented through a concise decision narrative and an optional embedded PDF preview.
A growth case study exploring early retention, testable hypotheses, experiment design, guardrail metrics, and prioritization.
A concept PRD for a focused career-planning feature, including problem framing, scope, model behaviour, product risks, success metrics, and trade-offs.
A static front-end recreation of the YouTube interface, built to practice layout precision, spacing systems, and responsive structure with plain HTML and CSS — explicitly not a PM case study and not evidence of applying for technical PM/PO roles.
What it reinforced: attention to interface detail and grid discipline sharpens the questions I ask engineers and designers during product reviews.
View on GitHubThis portfolio will grow with more Product Manager, Product Owner, and Business Analyst case studies over time, using the same reusable PDF-preview system used above.
A fresh discovery narrative, framed and evidenced the same way as the two flagships above.
Requirements, process mapping, and operational-impact analysis on a real workflow.
Hypothesis design, guardrail metrics, and prioritization for a growth problem.
Interested in discussing product opportunities, research-led discovery, or business-analysis work? I would welcome a conversation.