Director, Customer Intelligence – Data Science, Machine Learning

Job not on LinkedIn

October 8

🗽 New York – Remote

Although this job is listed in New York, this company may be open to hiring remote in other states.

💵 $271k - $357k / year

⏰ Full Time

🔴 Lead

📊 Data Scientist

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Fanatics, Inc.

WebsiteLinkedInAll Job Openings

Gaming • Retail • eCommerce

Fanatics is building a leading global digital sports platform that aims to enhance the fan experience for over 100 million sports enthusiasts worldwide. The company operates across several divisions including Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming. They offer fans the opportunity to buy licensed fan gear, jerseys, lifestyle products, and headwear, collect physical and digital trading cards, sports memorabilia, and other digital assets, as well as engage in sports betting. Fanatics partners with over 900 sports properties globally, including major leagues, teams, and athletes, and operates more than 2,000 retail locations. The company is committed to corporate responsibility with a focus on philanthropy, diversity and inclusion, brand protection, and sustainability.

1001 - 5000 employees

Founded 2011

🎮 Gaming

🛒 Retail

🛍️ eCommerce

📋 Description

• Own strategy & portfolio. Define multiyear DS/ML strategy and prioritize a cross-team roadmap supporting a range of projects across personalization, marketing, and trading/risk. Communicate clear goals and success metrics to executives and partners. • Lead and set standards. Build and scale a high-performing small-but-mighty team of cross-functional experts. Establish succession plans, standards of excellence, and a feedback-rich, inclusive culture; set the bar for hiring, performance, and career development. • Deliver production ML. Ship reliable real-time and batch models (feature stores, offline/online training, CI/CD for ML, model registry, canary/shadow deploys, rollback). Establish model governance, documentation, and observability (data drift, bias/fairness, performance SLOs). • Operational excellence. Stand up on-call practices, incident response, post-mortems, and SLOs for data and model services. Drive cost efficiency (rightsizing compute, caching, autoscaling) while protecting customer experience. • Experimentation & causal inference. Scale an experimentation program (A/B, multi-armed bandits, CUPED/causal methods) with clear guardrails, review, and instrumentation to attribute impact through causal inference techniques. • Blend scientific and technical vision. Set credible and inspiring long-term research and scientific direction for data scientists, while maintaining the connection “from research lab to factory floor” between science and engineering. • Stakeholder leadership. Align with Product, Risk/Trading, Marketing, and Compliance; present strategy, risks, and results to execs in clear narratives and dashboards.

🎯 Requirements

• 10+ years in Data Science/Applied ML (or equivalent) with 5+ years leading senior ICs and/or managers; proven delivery of ML products at scale. • Expertise across predictive modeling, ranking/recommendation, and/or time-series/forecasting • Excellence in written and verbal communication; capable of driving cross-org decisions with clear narratives and data. • Experience launching/kickstarting 0-to-1 solutions, esp. dealing with high ambiguity and being a proactive change agent in face of decision deadlocks or unclear next-steps • Strong product sense and business judgment. • Experience in regulated industries (fintech/gaming) and real-time decisioning at scale. • Hands-on depth with Python, SQL/PySpark, ML frameworks (scikit-learn/XGBoost/TensorFlow/PyTorch), and MLOps (feature stores, MLflow/model registry, CI/CD, online serving). • Experience deploying econometric and/or causal inference techniques at scale through software and systems (going beyond just analytics and reporting). • Experience building a high-performing blended cross-functional team of scientists and engineers, working together as one team with shared goals and incentives. • Cloud platform expertise (AWS preferred), containers/Kubernetes, and infrastructure-as-code. • Advanced degree in CS/EE/Stats/Math/Econ (or equivalent applied experience).

🏖️ Benefits

• Medical • Dental • Vision • 401K • Paid time off • GymPass • Pet Insurance • Family Care Benefits • Home office setup allowance