Modeling & Analysis

Mathematical Modeling and Machine Learning Link to heading

We develop Machine Learning models and Monte Carlo simulations to solve complex problems requiring quantitative analysis, prediction, or optimization — always with a focus on production deployment and concrete business impact in domains like finance, sports betting, and SaaS platforms.

Our Experience Link to heading

We have proven experience in financial applications and sports betting, where precision and speed are fundamental. This experience allows us to develop robust models for any domain requiring quantitative analysis.

What We Do Link to heading

Monte Carlo Simulations

Risk analysis and financial options valuation
Pricing and risk management for sports betting
Forecasting with uncertainty for financial markets
Stochastic models for biology and epidemiology
Cellular automata for complex systems modeling
Strategy optimization under uncertainty
Sensitivity analysis and what-if scenarios

Machine Learning

Predictive models for forecasting in finance and e-commerce
Classification and anomaly detection for betting and fraud
Recommendation systems for retail and content
Price optimization and trading strategies
Time series analysis for financial markets and demand
Feature engineering and modeling for business applications

Data Analysis

Exploratory Data Analysis (EDA)
Complex data visualization
Pattern and correlation identification
Data pipeline design and ETL
Interactive dashboards with Grafana or custom solutions

Mathematical and Combinatorial Optimization

Linear and non-linear programming
Portfolio optimization
Graph problems: shortest path, max flow, minimum cost flow
Network flows: network design, assignment problems
Scheduling: job shop, vehicle routing, resource allocation
Constraint satisfaction and integer programming
Genetic algorithms and metaheuristics

Use Cases by Industry Link to heading

Finance

Derivatives pricing with Monte Carlo
Value at Risk (VaR) calculations
Portfolio optimization with constraints
Fraud detection with ML
Credit scoring models

E-commerce and Retail

Demand forecasting
Price optimization
Customer churn prediction
Inventory optimization
Recommendation systems

Logistics and Supply Chain

Route optimization
Inventory management
Demand forecasting
Resource allocation
Disruption simulation

Gaming and Betting

Odds calculation with Monte Carlo
Player behavior prediction
Risk management
Fraud detection
Real-time pricing

Oil & Gas

Reservoir simulation and uncertainty analysis
PVT analysis (pressure, temperature, viscosity) and fluid properties modeling
Production operations optimization (wellbore, electric submersible pumps - ESP, tubing, separator)
Numerical modeling, calibration, and what-if scenarios

Technology Stack Link to heading

Languages and frameworks

R: tidyverse, caret, ggplot2, Shiny — for statistical analysis and visualization
Python: scikit-learn, pandas, NumPy, SciPy
C++/Go/Rust: For high-performance production models

Key technologies:

Infrastructure

Distributed computing with Dask or Ray
GPU acceleration with CUDA when needed
Databricks or Snowflake for data warehousing
MLflow for experiment tracking

Visualization

Matplotlib, Seaborn, Plotly for analysis
Grafana for production dashboards
Custom web dashboards with React when needed

Our Process Link to heading

1. Problem Definition

Understand the business problem
Define success metrics
Evaluate feasibility and expected ROI

2. Data Exploration

Exploratory analysis of available data
Identify data quality issues
Initial feature engineering

3. Model Development

Iterative model development
Validation with appropriate cross-validation
Hyperparameter tuning
Trade-off analysis (accuracy vs interpretability vs performance)

4. Production Deployment

Model in production with monitoring
A/B testing when appropriate
Automated retraining pipeline
Complete documentation

Why our approach is different? Link to heading

Pragmatism over hype We don’t use deep learning when linear regression is sufficient. We prioritize solutions that work in production, not academic papers.

Performance matters Our models run in production with < 100ms latencies when necessary. We optimize for real performance, not just accuracy.

Interpretability We understand that models must not only predict well but be explainable. We prioritize interpretability when the use case requires it.

Production-ready We don’t just make Jupyter notebooks and leave. We deploy models in production with monitoring, alerting, and automated retraining.

Project Examples Link to heading

Dynamic pricing system for sports betting

Model: Monte Carlo simulation + ML for odds adjustment
Latency: < 50ms for real-time pricing
Result: 15% reduction in risk exposure

Demand forecasting for e-commerce

Model: Ensemble of ARIMA + XGBoost + LSTM
Accuracy: MAPE < 8% on 30-day forecast
Result: 25% reduction in stock-outs

Portfolio optimization for fund manager

Model: Mean-variance optimization with Monte Carlo for VaR
Constraints: Regulatory + client-specific
Result: 12% improvement in Sharpe ratio vs benchmark

When do you need modeling? Link to heading

You have data and want to extract insights or predictions
You need to make decisions under uncertainty
You need to optimize resources or processes
You want to automate decisions that are currently manual
You need to quantify risk or uncertainty