I am an applied decision scientist building uncertainty-aware forecasting and decision support systems for high-stakes, data-scarce environments. I specialize in Bayesian modeling, inventory analytics, and human-in-the-loop workflows across regulated domains such as biotech and healthcare.
My work emphasizes principled modeling, honest failure analysis, and designing analytical systems that make automated inference workflows reliable under uncertainity.
Most data science portfolios optimize for predictive accuracy in static benchmark settings. My work instead focuses on decision-grade reliability in real operational environments characterized by sparse data, nonstationarity, and asymmetric failure costs.
Across projects, I design systems that:
- Explicitly model uncertainty rather than collapsing it into point predictions
- Encode operational constraints into decision rules
- Intentionally throttle automation when model confidence is insufficient
- Integrate human oversight as a first-class component of the system
- Treat model failure as a diagnostic signal about system structure, not a defect to be hidden
This perspective is informed by experience in regulated domains where false confidence, silent failure, and automation bias carry real-world consequences.
This project explores stochastic inventory forecasting under severe covariate scarcity using Poisson–Gamma conjugacy and a waste-constrained restocking policy.
It demonstrates both a principled Bayesian modeling approach and the structural limits of automated forecasting in nonstationary, human-driven consumption systems.
Key contributions:
- Exposure-normalized Poisson–Gamma modeling of consumption rates
- Closed-form posterior predictive forecasting via Negative Binomial distributions
- Monte Carlo decision policy optimizing reorder quantities under waste constraints
- Rolling-origin evaluation under regime shifts
- Formal falsification of automation viability under nonstationarity
Artifacts:
- 📄 Methods Note (PDF): https://thefifthpostulate.github.io/projects/stochastic-forecasting.html
- 📊 Analysis Notebook: https://thefifthpostulate.github.io/Stochastic-Consumption-Forecasting/InventoryProject.html
- 💻 Source Code: Available upon request
Key takeaway:
Uncertainty modeling revealed the true complexity of the consumption process. Assumptions about the stochastic process and decision rule were insufficient to consistently provide decision-grade forecasts, making expert oversight more reliable than fully automated inventory control.
Jithakrishna Prakash
📧 jprakashoff@gmail.com
🔗 LinkedIn: https://linkedin.com/in/jithakrishna-prakash
💻 GitHub: https://github.com/TheFifthPostulate