Work With Me
I help teams make forecasting, pricing, and ML outputs more trustworthy, explainable, and usable in real decisions.
Most teams do not need more AI first. They need to know:
- Can we trust the forecast?
- Where does the model fail?
- Is the output biased?
- Is the advanced model actually better?
- Can business users explain and challenge the recommendation?
Best Fit
I am a strong fit for teams that already have forecasting, pricing, or ML workflows, but need stronger evaluation, uncertainty handling, and decision logic.
Typical problems:
- forecasts are generated but not fully trusted
- model performance looks good on average but fails in specific segments
- uncertainty, bias, and robustness are not visible enough
- pricing recommendations need clearer guardrails
- ML workflows need better evaluation or deployment readiness
Main Offer
Forecasting Trust Diagnostic
A focused review for teams that already generate forecasts, but are unsure where the system is reliable, biased, fragile, or operationally weak.
Typical outputs:
- baseline and model comparison
- rolling backtest evaluation
- error and bias breakdown
- segment-level failure analysis
- uncertainty/calibration review
- recommended model-selection policy
- next-step roadmap
Goal:
What can we trust, where does it fail, and what should we improve next?
Other Areas
I am also open to selected conversations around:
- pricing analytics
- probabilistic forecasting
- conformal uncertainty validation
- decision intelligence prototypes
- MLOps / AI Operations reviews
Portfolio
Contact
Email: pranav.systems@proton.me
LinkedIn: linkedin.com/in/topranav
Compliance Note
Independent portfolio content only. Views are my own. No proprietary or confidential employer information is included.