Director, Customer Intelligence (Data Science & Machine Learning)

Fanatics•New York, NY

23d

About The Position

As the Director of the Customer Intelligence DS/ML Platform team, you’ll lead a combined DS/ML group that builds, deploys, and operates production ML centered around deploying Customer Intelligence across multiple domains within Fanatics Betting & Gaming. You’ll set the multi-year strategy for applied science and platform capabilities, foster a culture of psychological safety and continuous learning, and drive measurable business outcomes through experimentation, rigorous analytics, and reliable, cost-efficient ML systems. As the leader of a growing cross-functional team, you will have significant room to explore and influence the future trajectory of numerous FBG and cross-Fanatics initiatives and peer teams, where effective Customer Intelligence applied through the right mechanisms, has the potential to be a game-changing competitive differentiator to supercharge FBG product offerings that delight and engage our fans, optimize marketing effectiveness, and bolster core unit economics for the business.

Requirements

10 or more years in Data Science/Applied ML (or equivalent) with 5 or more years leading senior ICs and/or managers; proven delivery of ML products at scale.
Cross-functional leadership of ML, data, and software engineering, in close partnership with technical and business teams across an organization’s internal DS/ML ecosystem
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

Nice To Haves

Experience in regulated industries (fintech/gaming) and real-time decisioning at scale.
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
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).
Cloud platform expertise (AWS preferred), containers/Kubernetes, and infrastructure-as-code.
Advanced degree in CS/EE/Stats/Math/Econ (or equivalent applied experience).

Responsibilities

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.