Fanatics

Compensation

Full Description

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