I build data systems that solve real-world problems. My core belief is simple: a brilliant data model is useless if it’s stuck in a notebook. My focus is getting those insights out of the lab and into your daily operations where they can make a tangible impact.
My Perspective: An Engineer’s Approach
My path here started in academia, where I was an Assistant Professor of Electrical Engineering teaching the mathematical theory behind forecasting. That background gave me a deep appreciation for analytical rigor, but I learned a hard lesson when I moved into the corporate world: a model that’s perfect on paper can still fail in practice.
I saw a sophisticated forecasting project struggle not because the math was wrong, but because the system around it couldn’t keep up. The data was unreliable, and the deployment process was fragile.
That experience shaped my entire career and led me to a foundational principle: great data science requires great engineering.
How I Think
When I approach a new challenge, I don’t start with the algorithm. I start with strategic, systems-level questions to understand the root of the problem and ensure we’re building a solution for the long term.
- How will we guarantee the integrity and quality of the data from end to end?
- How do we build a system that can be deployed, updated, and retrained reliably, without needing constant manual oversight?
- Are we just solving today’s immediate problem, or are we building a scalable foundation for what’s next?
Answering these questions first is the only way to build data products that are resilient, maintainable, and deliver value long after the project is launched.
Theory into Practice: Real-World Results
This isn’t just theory. I’ve applied this systems-first approach to deliver measurable results for major companies:
- At Intuit, I architected predictive models that optimized staffing for over 6,000 agents, leading to better customer and agent experience.
- At Shipt, I automated the forecasting pipeline for customer service demand, improving accuracy by 45% and cutting the reforecasting cycle time by 30%.
- At GoDaddy, I embedded new analytic models into their capacity plans for 1,000+ support queues on their entire product stack, increasing forecast precision by 25% and reducing overstaffing costs.
Let’s Talk
If you’re wrestling with the challenge of moving from data analysis to automated, reliable data products, we should talk. I help organizations build the scalable solutions and forecasting capabilities they need to grow.
The best way to start the conversation is on LinkedIn.
Connect with me on LinkedIn: Patricio Parada | LinkedIn
Core Expertise: Predictive Analytics | Time-Series Forecasting | Data Architecture | MLOps | Workforce Optimization | Python | Spark | Databricks | SQL