Toronto · Remote-friendly · 7+ years in product & analytics

Hi, I'm Parisa.

I work at the intersection of AI, data, and product — bringing experimentation discipline, analytics depth, and a builder's instinct to teams shipping AI-powered products.

I combine product thinking, analytics, research, and execution discipline to bring structure to complex problems and help teams move from scattered signals to clear product decisions.

Product StrategyAnalyticsResearchExperimentationStakeholder AlignmentSystems Thinking
Operating snapshotLive
Focus
Product management
Strengths
Strategy · Analytics · Experimentation
Domains
Banking · Ad-tech · Marketplace · Ops
Style
Signal → Decision → Outcome

I bring clarity, structure, and momentum to ambiguous environments.

My work often sits between business, data, users, operations, and technology. I help define the real problem, organize the signals, align stakeholders, and turn ideas into systems, workflows, experiments, dashboards, or product improvements that create measurable impact.

Module 01

Product Strategy

Framing problems, defining priorities, clarifying trade-offs, and connecting product decisions to business outcomes.

Module 02

Analytics & Decision Support

Using data, dashboards, funnels, metrics, and research signals to make better decisions.

Module 03

Experimentation & User Insight

Testing assumptions through experiments, user research, behavioral analysis, and structured learning.

Module 04

Execution Systems

Creating the operating rhythms, documentation, Jira/Confluence systems, intake flows, and stakeholder alignment needed to move work forward.

7 stages · From ambiguity to outcome

From ambiguity to outcome.

Ambiguity → Signal → Problem → Decision → Build → Outcome → Learning

01

Ambiguity

Where the work starts: unclear requests, scattered data, stakeholder confusion, operational friction, or a problem that has not been clearly defined.

02

Signal

I collect signals from users, data, dashboards, support tickets, operations, stakeholder conversations, research, and business context.

03

Problem

I turn scattered signals into a clear problem statement, success criteria, constraints, and user/business context.

04

Decision

I prioritize what matters, clarify trade-offs, align stakeholders, and define the next best move.

05

Build

I help create the system, workflow, dashboard, experiment, process, or product improvement that moves the team forward.

06

Outcome

I measure what changed, identify what worked, and connect the work back to business or user impact.

07

Learning

I turn outcomes into learning, next iterations, and better product judgment.

This is the operating system underneath every case file — the same thinking shows up across banking, ad-tech, supply chain, marketplaces, and research.
View all case studies →

One thinking system, applied across industries.

The same product thinking system shows up across banking, ad-tech, supply chain, marketplaces, and research.

Case File 01Banking

TD Bank

Banking · Authentication · Product Ops

Ambiguity

A high-stakes authentication team with no Jira, no intake, and requests buried in scattered emails.

System built

Stood up Jira to TD standards, built a Confluence intake that auto-creates structured tickets, authored the knowledge base, and introduced a weekly reporting rhythm.

Outcome

~30% less stakeholder back-and-forth; EVP-level visibility into the team's work.

Read case study →
Case File 02Ad-Tech

Yektanet

Ad-Tech · Product Management

Ambiguity

25M+ users, manual CRM, a leaky advertiser UX, rule-based retargeting, and an un-monetized creator tool.

System built

Lifecycle automation for 20,000+ accounts, ticket-pattern-driven UX fixes, dynamic-segmentation experiments, and a 0→1 e-commerce motion.

Outcome

~30% growth in average daily ad revenue and ~1,000 customers converted before pivot.

Read case study →
Case File 03Supply Chain

Canadian Tire

Supply Chain · Operations Analytics

Ambiguity

Persistent late freight and set-to-ship exceptions at Canada's largest distribution centre — with no real-time view of either.

System built

Shipping metrics defined, SQL/Oracle queries, Power BI dashboards with control-chart thresholds, A3/Six Sigma analysis, and a self-initiated Python model.

Outcome

~20% reduction in set-to-ship exceptions; AVP-level presentation linking late freight to dealer claims.

Read case study →
Case File 04Marketplace

SnappCarFix

Marketplace · Growth · Analytics

Ambiguity

An early-stage marketplace running campaigns with no analytics, no dashboards, and no vendor visibility.

System built

End-to-end ownership of monthly campaign P&L while standing up the company's first dashboards, data pipelines, and vendor performance views.

Outcome

10–20% above target every month; ~10% reduction in marketing cost.

Read case study →

Let's build products with clarity.

If you are working on complex digital products, customer journeys, analytics systems, or ambiguous business problems, I'd be happy to connect.