We are seeking analytics engineers with a passion for sports to implement, automate, and optimize the quantitative models that power our world-class sports intelligence platforms in baseball, basketball, cricket, football (American), hockey, soccer, and tennis. Through your work, you can support the professional teams in our exclusive partner network in their efforts to compete and win championships. We currently have both entry-level and senior positions available, allowing us to consider qualified candidates with a wide range of experience levels.
Zelus Analytics (https://zelusanalytics.com) unites a fast-growing startup environment with a research-focused culture that embraces our core values of integrity, innovation, and inclusion. We pride ourselves on providing meaningful mentorship that offers our team the opportunity to develop and expand their skill sets while also engaging with the broader analytics community. In so doing, we hope to create a new path for a broader group of highly talented people to push the cutting edge of sports analytics.
We believe that a diverse team is vital to building the world’s best sports intelligence platform. Thus, we strongly encourage you to apply if you identify with any marginalized community across race, ethnicity, gender, sexual orientation, veteran status, or disability. At Zelus, we are committed to creating an inclusive environment where all of our employees are enabled and empowered to succeed and thrive.
As a Zelus Analytics Engineer, you will be expected to:
- Develop, validate, and automate quantitative models using statistics, machine learning, optimization, and simulation
- Develop, schedule, monitor, and maintain model training and prediction workflows
- Coordinate with broader engineering team to plan and implement changes to core infrastructure to support one or more sports
- Collaborate with data scientists to define and manage model productionalization and platform release plans
- Deploy REST APIs on top of fitted models using distributed computation to support real-time, client-facing integration
- Collaborate and communicate effectively in a distributed work environment
- Fulfill other related duties and responsibilities, including rotating platform support
In addition to the above, a Senior Analytics Engineer will be expected to:
- Research, design, and test cloud-based computational environments to support quantitative modeling at scale
- Collaborate with product and data science leads to define and manage implementation, validation, deployment, and release plans/logistics for our sports intelligence platforms
- Break down complex engineering projects into actionable work plans including proposed task assignments for one to four engineers and data scientists
- Provide guidance and technical mentorship for junior engineers
- Assist with recruiting and outreach for the engineering team, including building a diverse network of future candidates
A qualified entry-level candidate will be able to demonstrate several of the following and will be excited to learn the rest through the mentorship provided at Zelus:
- Academic and/or industry experience in back-end software design and development
- Academic, industry, and/or research experience with applied mathematical and predictive modeling (statistics, machine learning, optimization, and/or simulation)
- Experience with cloud infrastructure and distributed computing
- Fluency with Python (preferred), R, Scala, and/or other data-oriented and statistical programming languages
- Experience with relational databases and SQL development
- Familiarity working with Linux servers in a virtualized/distributed environment
- Strong software-engineering and problem-solving skills
A qualified senior candidate will be able to demonstrate all of the above at a higher level of competency plus the following:
- Expertise designing, developing, and optimizing the cloud infrastructure for large-scale, cloud-based analytics systems
- Experience with task orchestration and workflow automation tools (Airflow preferred)
- Experience adapting, retraining, and retooling in a rapidly changing technology environment
- Desire and ability to successfully mentor junior engineers
Zelus has a predominantly distributed workforce, spanning nine states and four countries, with a central hub in Austin, Texas. In addition to competitive salaries, our compensation packages include equity and benefits, such as 401(k) matching and unlimited PTO, that allow us to attract and retain a world-class team.
As an equal opportunity employer, Zelus does not discriminate on the basis of race, ethnicity, color, religion, creed, gender, gender expression or identification, sexual orientation, marital status, age, national origin, disability, genetic information, military status, or any other characteristic protected by law. It is our policy to provide reasonable accommodations for applicants and employees with disabilities. Please let us know if reasonable accommodation is needed to participate in the job application or interview process.
Zelus is an at-will employer; employment at Zelus is for an indefinite period of time and is subject to termination by the employer or the employee at any time, with or without cause or notice.
Job Type: Full-time
Pay: $75,000.00 - $150,000.00 per year
- 401(k) matching
- Dental insurance
- Disability insurance
- Flexible schedule
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Parental leave
- Vision insurance
- Are you currently authorized to work in the United States? If not, in which countries are you authorized to work? Would you require sponsorship for this position, now or in the future?
- Why are you interested in an analytics engineer position at Zelus Analytics? (50 words or less)
- Would you like to be considered for a junior position, a senior position, or both?
- What Python and/or R packages for data science do you most frequently use? What do you like or dislike about those tools? (50 words or less)
- Describe a project you’ve worked on for which you utilized virtualization or containerization to develop and/or deploy a quantitative model. What roadblocks did you encounter and how did you overcome them? (150 words or less)
- What does it mean to you to work for a company that values diversity and inclusion? Please feel free to draw on and share personal experiences. (150 words or less)
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