Fanatics Betting & Gaming
Machine Learning Engineer III - (Remote)
New York, NY, United States · Mid · Full-time
Sponsorship not specified$117k-$200kDetected 14 hours ago
PythonDistributed SystemsSQLDatabricksAWSKafkaMachine LearningSparkData EngineeringData ScienceMLOpsStatisticsA/B Testing
About the role
- We are the Fan Ecosystem Data team, responsible for enhancing decision-making and innovation across the entire Fanatics ecosystem through data and analytics.
Responsibilities
- Own the end-to-end ML infrastructure for recommendation, personalization, and LTV scoring systems, from feature engineering through model deployment and monitoring.
- Build and maintain real-time and batch feature pipelines that serve low-latency predictions across the FanApp recommendation experience and cross-vertical personalization use cases.
- Develop and scale model serving infrastructure that supports high-throughput, high-availability prediction across Fanatics' multi-product ecosystem.
- Partner directly with Data Scientists to productionize LTV, churn, propensity, and ranking models and bridge the gap between experimentation and reliable production systems.
- Build and maintain embedding pipelines that generate and refresh user and item representations powering personalization and affinity modeling at scale.
- Implement and maintain A/B testing and experimentation infrastructure that enables reliable measurement of model and feature impact in production.
- Collaborate with Data Engineers, Analytics Engineers, and Product teams to identify data sources, enforce data quality standards, and ensure models are fed with accurate, timely signals.
- Drive continuous improvement of model accuracy, latency, and throughput through iterative optimization and monitoring frameworks.
- Proven experience building real-time feature pipelines and model serving systems that operate at scale with strict latency and uptime requirements.
- Experience building or scaling recommendation or ranking systems in production, including embedding pipelines and low-latency inference infrastructure.
Requirements
- Strong Python proficiency and deep familiarity with production ML workflows, including packaging, versioning, deployment, and monitoring.
- Hands-on experience with end-to-end ML platforms such as Databricks, AWS SageMaker, or equivalent, including model registry and serving components.
- Strong SQL proficiency and experience working with relational and dimensional data models.
- Experience with feature stores (e.g. Feast, Tecton) and their role in supporting both real-time and batch ML use cases
- Experience with ML observability tooling, including drift detection, prediction monitoring, feature freshness alerting
Nice to have
- Preferred But Not Required
Compensation
- $117,000 - $200,000 USD
Benefits
- For information about our benefits, please visit https://benefitsatfanatics.com/
- In addition to the base and bonus, full-time employment, and more.
- 3-5+ years in a machine learning engineering or data engineering role, with a degree in a quantitative field (Computer Science, Mathematics, Statistics, Engineering, or equivalent).
Company info
- Fanatics is building a leading global digital sports platform. We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect, and Bet. Through the Fanatics platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods
- collect physical and digital trading cards, sports memorabilia, and other digital assets
- and bet as the company builds its Sportsbook and iGaming platform. Fanatics has an established database of over 100 million global sports fans
- a global partner network with approximately 900 sports, including major national and international professional sports leagues, players associations, teams, colleges, college conferences and retail partners, 2,500 athletes and celebrities, and 200 exclusive athletes
- Our more than 22,000 employees are committed to relentlessly enhancing the fan experience and delighting sports fans globally.
- Fanatics is building a leading global digital sports platform.
- We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect, and Bet.
- Through the Fanatics platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods; collect physical and digital trading cards, sports memorabilia, and other digital assets; and bet as the company builds its Sportsbook and iGaming platform.
- Fanatics has an established database of over 100 million global sports fans; a global partner network with approximately 900 sports, including major national and international professional sports leagues, players associations, teams, colleges, college conferences and retail partners, 2,500 athletes and celebrities, and 200 exclusive athletes; and over 2,000 retail locations, including its Lids retail stores.
- We build products that turn disparate data streams into real-time actionable insights, empowering teams to unlock greater value for our customers and stakeholders across every Fanatics surface.
- We are seeking a Machine Learning Engineer III to own the infrastructure and systems that bring our data science models to life at scale.
- As our Data Scientists and Data Engineers build the models that understand and predict fan behavior, you build the platforms that serve those models in production.
- These sessions are designed to build connection and bring our culture to life, though specific travel and participation requirements will be confirmed based on your role and location.
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This listing is sourced directly from Fanatics Betting & Gaming's careers page and normalized into a canonical job model.