Company Description
Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients.
Duties:
Work closely with Data Scientists and Engineers to diagnose and treat data pipeline integrity issues
Detect data inaccuracies such as missing, out of range or otherwise incorrect on-field data
Source origins of data inaccuracies through data pipeline dependencies and python code base
Define data validation tests to flag future game errors
Research accurate roster active statuses, primary positions and game participation
Validate data changes after logic updates
Production model feature deep dives to explain project market lines
Clearly document findings
Develop intimate familiarity with existing databases and construct metadata references
With guidance, support lead Data Scientists in feature development and model analysis
Requirements:
Bachelor's Degree in Computer Science, Data Science or similar major
Minimum of 1 year of experience in football data analysis
Deep knowledge of football, basketball or baseball; including roster compositions of professional and college teams, general gameplay strategies, and typical in-game scenarios
Data Extraction, Wrangling and Analysis in Python
Strong SQL querying skills
Attention to detail
Preferred:
Strong Python data management programming skills
Data Visualization experience with a user application like Streamlit
Deep knowledge of a second sport including football, basketball, baseball, hockey or tennis
Exposure to the data science process and tech stack
Anomaly Detection Techniques
Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employer’s discretion, this position may require successful completion of background and reference checks.
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