Data Scientist at Swish Analytics

Job description

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. Job Description You'll be joining a team that is working to develop the infrastructure for a system to optimize our simulation outputs based on a variety of external and internal signals. This role will work at the intersection of data science, trading, and data engineering to help develop and maintain trader automation algorithms that will react faster to signals as well as improve model accuracy. This position is remote from the USA or Canada. Duties: • Develop infrastructure for trader automation and system performance tracking • Develop high-performance and low-latency products to react to external and internal signals • Analysis of live-streaming data and turning it into actionable decisions • Design and set up tests to detect unexpected changes to our models resulting from manual interactions. • Use extensive experience to build, test, debug, and deploy production-grade components • Ideate, develop, and improve machine learning and statistical models that drive Swish's core algorithms, growing into top-level simulation output modeling as we expand to new sports. • Develop contextualized feature sets that draw on sports-specific domain knowledge. • Contribute across all stages of model development — from proof-of-concept and beta testing to partnering with data engineering and product teams to deploy new models. • Constantly improve model performance using insights from rigorous experimentation. • Assess model performance, identify weaknesses, and use those findings to direct development efforts. • Document your work and present it clearly to technical and non-technical partners. Requirements • Bachelor's in Data Science, Statistics, Computer Science, Applied Math, or a related technical field; Master's strongly preferred. • 4+ years developing and delivering effective machine learning and/or statistical models to serve real business needs in sports analytics or sports betting. • Experience in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, and Markov Chain Monte Carlo methods. • Excellent analytical and problem-solving ability, and a demonstrated drive to learn quickly in unfamiliar territory. • Experience with Python and relational SQL. • Strong foundation with source control (GitHub) and related CI/CD processes. • Strong foundation working in AWS environments. • Ideal candidates will have experience with Kafka, Docker, and Kubernetes • Ability to partner across teams on complex, ambiguous problems and communicate clearly with technical and non-technical audiences. Base salary: Starting at $150,000 - DOE 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 those outlined above and are subject to change. At the employer's discretion, this position may require successful completion of background and reference checks.

Requirements

Must-haves

Company Description
Develop infrastructure for trader automation and system performance tracking
Develop high-performance and low-latency products to react to external and internal signals
Analysis of live-streaming data and turning it into actionable decisions
Design and set up tests to detect unexpected changes to our models resulting from manual interactions.
Use extensive experience to build, test, debug, and deploy production-grade components
Ideate, develop, and improve machine learning and statistical models that drive Swish's core algorithms, growing into top-level simulation output modeling as we expand to new sports.
Develop contextualized feature sets that draw on sports-specific domain knowledge.
Contribute across all stages of model development — from proof-of-concept and beta testing to partnering with data engineering and product teams to deploy new models.
Constantly improve model performance using insights from rigorous experimentation.
Assess model performance, identify weaknesses, and use those findings to direct development efforts.
Document your work and present it clearly to technical and non-technical partners.
4+ years developing and delivering effective machine learning and/or statistical models to serve real business needs in sports analytics or sports betting.
Experience in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, and Markov Chain Monte Carlo methods.
Excellent analytical and problem-solving ability, and a demonstrated drive to learn quickly in unfamiliar territory.
Experience with Python and relational SQL.
Strong foundation with source control (GitHub) and related CI/CD processes.
Strong foundation working in AWS environments.
Ideal candidates will have experience with Kafka, Docker, and Kubernetes
Ability to partner across teams on complex, ambiguous problems and communicate clearly with technical and non-technical audiences.
Base salary: Starting at $150,000 - DOE

Nice-to-haves

Bachelor's in Data Science, Statistics, Computer Science, Applied Math, or a related technical field; Master's strongly preferred.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

reactmachine learningpythonsqlci/cdawsdockerkubernetes

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