About Daksh Vanshaj¶
I'm a Data Scientist & MLOps Engineer who builds ML systems that actually make money—not just impressive demos that never leave the lab.
At 22, I've learned one hard truth: most ML projects fail not because of bad models, but because they don't solve real business problems. That's why I start with ROI, not algorithms.
My Core Philosophy¶
Machine Learning is only valuable when it cuts costs or boosts revenue. I don't build "models in a vacuum"—I build production systems that deliver measurable business impact:
🚀 ROI-Driven
Every project starts with a feasibility report showing estimated impact in dollars. No guessing. No "let's train a model and see what happens."
⚡ Modern & Lean
I use high-performance, cost-effective tools (Polars, DuckDB) over bloated legacy stacks. Faster pipelines = lower cloud bills = higher margins.
🛡️ Production-Ready
Automated testing, drift monitoring, CI/CD pipelines—non-negotiable. Your system runs reliably without constant firefighting.
What I Bring to Your Business¶
Data Engineering
Polars, DuckDB, PySpark, Delta Lake, DVC—I build pipelines that handle millions of rows without breaking your budget.
Machine Learning
XGBoost, Scikit-learn, Optuna, SHAP—I pick the right algorithm for your problem, not the trendiest one.
MLOps
MLflow, BentoML, FastAPI, Evidently AI, Kubernetes—your models deploy once and run forever (with monitoring so you know when they break).
Business Translation
ROI modeling, feasibility reports, stakeholder communication—I speak both Python and profit margins.
How I Work with You¶
I don't claim to know your industry better than you do. Here's my honest process:
1. I Listen
You explain the problem. I ask about constraints, KPIs, and what you've already tried.
2. I Research
I study your domain, explore 2-3 solution approaches (ML, heuristics, or simple automation), and assess feasibility.
3. I Prove It First
Before writing production code, you get a feasibility report with rough ROI estimates. No surprises later.
4. I Build & Deliver
End-to-end MLOps pipeline—automated, monitored, documented. You own it, not me.
Industry Agnostic
This process means I can tackle retail, logistics, SaaS, or manufacturing—because the process transfers, even when the domain changes.