Karthik Nadella
Data, Analytics and AI for Growing Companies
I've been lucky to work across companies of all sizes in different industries, always with the mission of helping people make better decisions with data.
At each of my previous stops, I've built internal data products and teams that gave the organization clarity on its most important problems. More importantly, I worked alongside the teams to actually solve those problems instead of just reporting on them.
Right now I'm at Pest Share, where I'm building a data platform for both human judgement and agentic workflows. The question I keep coming back to is how the two use cases interact and what that means for the shape of a modern data platform.
If you're wrestling with something similar, I'd enjoy hearing about it. You can book a 30-minute chat or email me.
What I like working on
- Data Architecture and Modelling. Clean, scalable designs, with a warehouse and a set of models where every number has one definition and one owner, so the foundation holds up as the company adds products, teams and tools.
- Actionable Metrics. Measurement that tells a team what to do next and makes progress easy to see. That usually means a small set of numbers, defined once, instrumented properly, and reviewed often enough that everyone can tell whether last month's change worked.
- Internal Data Products. Fully built tools that help a team run its operations better, such as labour and capacity planning, experimentation, and pricing, with the data, the logic and the workflow in one place so the decision gets made inside the tool.
Work
- 2026–
Pest Share · Data Platforms
Pest Share handles pest control for property managers. I joined with an initial mandate to build the data foundation. That means a single data warehouse with shared definitions, and then the internal data products on top of it, starting with pricing. It is early, which is the fun part, and I get to be curious about everything from how the ingestion runs to what an operator actually needs on screen.
- 2025–2026
Canada Goose · Analytics and Trade, Ecommerce
I led analytics and trade for the ecommerce team. For personal reasons, my time there was shorter than I had hoped. However, while I was there, I helped drive the narrative on the weekly performance of the ecommerce channel and made the case for increased funding for performance marketing to drive incremental and profitable revenue.
- 2022–2025
Kijiji · Head of Analytics & Revenue Operations
Canada's largest classifieds marketplace. I built the analytics organization, which meant self-serve dashboards, real engineering practice in the analytics codebase, and a rework of sales territories and incentives. What I'm proudest of is that the analysts stopped pulling reports and started shipping, using NLP and machine learning to improve search relevance, SEO visibility and conversion.
- 2021–2022
RBC Ventures · Data Products
We launched a customer-facing data product on top of credit card transactions that drove inbound leads for the partnerships team. I also rebuilt the data infrastructure underneath it, which cut storage costs and shortened the path from idea to shipped product.
- 2016–2021
Loblaw Digital · Data Science & Engineering, Online Grocery
I built and led the data science and engineering team for online grocery, working directly with store operations. New fulfillment technology doubled capacity, real-time labour forecasting improved productivity and customer satisfaction, and better order batching made picking faster. It taught me that the best analytics work happens next to the people doing the physical work.
- 2014–2016
Revenue Management Solutions · Analyst
My first job was analyzing demand and presenting pricing recommendations to the executive teams at McDonald's Canada and Europe. I learned early the power of narrative storytelling over the intricacies of technical analysis.