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About AIstats
AIstats is transforming the football data market by moving from 2D to 3D, powered by a cutting-edge Computer Vision and Machine Learning ecosystem that unlocks deep insights for both fans and professionals.
What we build
Advanced Football Analytics - a mobile app for football fans an- a mobile app for football fans and bettors that reached 100k MAU in less than a year, featuring advanced statistics powered by our proprietary ML and Computer Vision technologies.
Advanced Analytics for Football Clubs - a next-generation analytics platform helping football clubs, scouts, and agents make smarter decisions both on the pitch and in the transfer market. Thanks to our early leadership in skeletal tracking data, AIstats secured several Tier-1 club partnerships within months of launching the platform.
Responsibilities
- Develop and improve statistical and machine learning models
- Analyze large-scale datasets using Python and SQL
- Conduct statistical analysis, hypothesis testing, and experiment evaluation
- Build interpretable ML solutions and explain model behavior
- Collaborate with cross-functional teams on data-driven initiatives
Requirements
- 3+ years of experience as a Data Scientist
- Strong Python skills with NumPy, Pandas, and scikit-learn
- Solid SQL knowledge; R is a plus
- Strong understanding of probability theory and mathematical statistics
- Experience with statistical testing: t-test, χ², ANOVA, Mann-Whitney, Wilcoxon, KS test, bootstrap/permutation tests, Bonferroni and FDR corrections
- Hands-on experience with XGBoost, LightGBM, and CatBoost
- Understanding of Bayesian methods
- Experience with longitudinal/panel data, mixed/fixed/random effects models, and repeated measures
- Experience with interpretable ML approaches: GAM, SHAP, partial dependence, counterfactual analysis
Nice to Have
- Experience with deep learning: Transformers, attention mechanisms, RNN/LSTM
- Knowledge of Graph Neural Networks
- Experience with Reinforcement Learning: PPO, MAPPO, MAT, multi-agent RL
- Familiarity with Google Research Football or similar environments
- Advanced Bayesian statistics: PyMC, Stan, numpyro, MCMC, hierarchical models
- Experience with causal inference: DAGs, instrumental variables, difference-in-differences
- Knowledge of probabilistic spatial models: Markov chains, HMM/DBN, MRF/CRF, Gaussian processes
What we offer
- Fully remote work format;
- Flexible start of the working day and convenient schedule;
- Stable competitive salary pegged to the USD;
- 24 days of paid vacation and 15 additional paid days off (including sick leave, corporate holidays, and national/religious holidays);
- Friendly communication culture, great product, and transparent processes;
- Dynamic work environment with a passionate team that loves sports and technology;
- Opportunities for professional growth and career development;
- No micromanagement - just trust, autonomy, and freedom to experiment.
