Trading Analytics Manager (Data Scientist)

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 enterprise clients.

Job Description - This position is 100% Remote

Swish Analytics is looking for a hands-on Trading Analytics Manager to join our ever-growing team! Data Science is at the core of our business, so this team has true ownership and impact over developing core components of Swish's data products.

Duties:

• Work with a team of data professionals to execute on the following deliverables:
• Apply statistics and data modeling to gain actionable business insights for Trading and Data Science Teams.
• Own the end-to-end data pipeline that will be used for extracting, managing, and analyzing customer data from multiple sources.
• Develop algorithms to provide automated trading suggestions that maximize margin and minimize risk.
• Communicate and track key performance metrics across Trading and Data Science.
• Manage cross-functional efforts, review their performance, and manage schedules.
• Mentor others on the team while owning independent deliverables.
• Develop and generate reports of findings and present these to senior management.

Requirements:

• Master’s Degree/PhD in Statistics, Mathematics, Computer Science or related STEM Field
• 5+ years of financial risk management modeling (sports betting, hedge fund management, options trading, securities trading, etc)
• Advanced level skills in Python to ingest, clean, model and visualize data
• Understanding of the sports betting marketplace or experience analyzing sports data
• Proven ability to train/mentor team members in utilizing best practices for software development as well as data science implementations
• Experience using visualization tools (plotly, Tableau, etc)
• Experience using Git (or other version control systems) in a collaborative environment with other developers
• Experience in developing or helping design (in collaboration with data engineers) ETL processes that are used to generate cleaned and processed data as input for data science modeling or analysis
• Writing complex SQL queries to efficiently retrieve data from relational databases (e.g. MySQL, Postgres)