Applied Mathematician to $6500

Quantitative Researcher / Applied Mathematician
 

About BetWizard:

BetWizard is a research-driven applied project focused on predicting outcomes of esports competitions.

We develop our own probabilistic, statistical, and ML-based models, built on the analysis of:

• large-scale historical match data,
• in-game telemetry and event data,
• bookmaker odds dynamics and market price movements.

The project is not about reporting, dashboards, or data visualization.
Our core focus is deep quantitative analysis, identification of structural market inefficiencies, and building alternative probability estimates.

A key challenge is working with non-stationary processes, such as:

• game patches and meta shifts,
• roster changes,
• player form dynamics.
   

Work Format:

• Fully remote
• Flexible schedule, results-oriented
• Long-term collaboration
   

Who We’re Looking For:

We are looking for an Applied Mathematician / Quantitative Researcher who is comfortable working with uncertainty and probabilities and knows how to turn data into robust predictive models.

This is not a classic Data Analyst role and not a pure ML Engineer position.
 

Key Responsibilities:

• Develop and improve probabilistic models for esports match outcome prediction
• Analyze and model bookmaker odds and their dynamics
• Identify systematic market biases and value betting scenarios
• Work with time series and non-stationary data (patches, meta shifts, team form)
• Apply Bayesian approaches for probability updating
• Validate, test, and interpret models
• Integrate ML methods into statistical models when appropriate
   

Required Mathematical Background:

• Probability theory
• Mathematical statistics
• Bayesian statistics
• Regression models (GLM, logistic regression)
• Time series analysis
• Stochastic processes
• Optimization basics (value betting, risk management)

⚠️ Purely theoretical mathematics (e.g., algebra, topology) without applied experience is not a fit.
 

Technical Stack (Expected Level)

• Python (NumPy, pandas, SciPy, statsmodels, scikit-learn)
• Experience working with large datasets
• Understanding of ML approaches and their limitations
• Experience building and evaluating predictive models
   

We Do NOT Consider

• ML Engineers without strong probability and statistics foundations
• Data Analysts without predictive modeling experience
• Junior Data Scientists
• Pure theoreticians without applied projects
   

Nice to Have:

• Experience in sports / esports betting or financial markets
• Understanding of bookmaker odds and margin mechanics
• Experience with real-time or near-real-time data
• Strong research skills and hypothesis formalization
   

What We Offer

• Fully remote work with no location restrictions
• Work on a real quantitative product, not a showcase
• Intellectually challenging problems
• Ability to influence modeling architecture and approaches
• Competitive compensation