Lead Statistician – Sports Prediction

Insight Fusion Analytics

8 years

0 Lacs

India

Posted:1 week ago| Platform:

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Skills Required

fusion analytics data finance forecasting engineering calibration ml model architecture design simulations metrics code statistics econometrics numpy scipy statsmodels pymc sql resampling analysis simulation communication learning tensorflow pytorch aws gcp azure portfolio

Work Mode

Remote

Job Type

Full Time

Job Description

Location:

Company Overview

Insight Fusion Analytics turns complex data into actionable insight for clients across finance, retail, and professional sport. Our sports-analytics unit builds predictive systems that transform raw match, athlete, and biomechanical data into winning strategies.

Role Summary

Lead Statistician with deep expertise in sports prediction

What You’ll Do

Model Architecture & Validation
– Design Bayesian and frequentist frameworks (hierarchical Elo, Poisson-Gamma, state-space models) and build leakage-proof cross-validation strategies.
Feature Engineering & Experimental Design
– Derive advanced spatio-temporal, biometric, and contextual features; run A/B and multivariate tests to quantify lift.
Uncertainty Quantification
– Produce calibrated predictive intervals, scenario simulations, and decision-theoretic metrics (Brier, CRPS, EVaR).
Mentorship & Review
– Set statistical standards, review code/notebooks, and mentor junior analysts.
Stakeholder Communication
– Translate complex statistical results into concise recommendations for coaches, product managers, and executives.

Must-Have Qualifications

8+ years
professional experience (or
PhD + 5 years
) in applied statistics, econometrics, or quantitative social science.
Documented track record building
sports prediction
systems.
Expert proficiency with
Python
(NumPy, SciPy, Pandas, statsmodels, PyMC/Stan) and SQL; R a plus.
Mastery of resampling methods, hierarchical models, time-series analysis, Monte-Carlo simulation, and causal inference.
Proven success preventing data leakage and look-ahead bias in live pipelines.
Strong communication skills for both technical and non-technical audiences.

Nice-to-Have

Familiarity with deep-learning frameworks (TensorFlow/PyTorch) for hybrid stat-ML architectures.
Experience deploying models on AWS, GCP, or Azure using containerized workflows.
Publications or conference talks in sports analytics (MIT Sloan, NESSIS, MathSport).

How to Apply

insightfusionanalytics@gmail.com

CV
highlighting sports-analytics projects and publications.
Portfolio or repo links
demonstrating end-to-end statistical modelling for sports prediction.
A
one-page brief
describing your proudest predictive model: objective, methodology, error analysis, and business impact.

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