Principal Data Scientist, Riot Games

• 10+ years of experience in ML/Applied AI
• 3+ years of experience in principal/staff-level technical leadership
• Experience with large-scale, real-time ML systems (recommendations, personalization, matchmaking)
• Expertise in graph ML, RL, and representation learning
• Proficiency in PyTorch, TensorFlow, JAX, and modern data/serving tools (Ray, Kafka, Flink, Redis)
• Strong grounding in A/B testing, experiment design, and experience metrics
• Track record of setting ML strategy and standards across teams

Desirable

• Riot Data Scientists combine their wide technical expertise across data processing, automation, machine learning ("ML"), artificial intelligence ("AI"), and experimental design to inform decisions and develop data-powered products
• As a Principal Data Scientist on the Publishing Platform Data team, you will define and drive the modeling architecture that powers personalization, matchmaking, and social experiences across Riot’s player ecosystem
• You’ll partner closely with Product, Data Engineering, and Software Engineering to transform large-scale social graph, matchmaking, and behavioral data into adaptive, fair, and player-centric AI systems
• Your work shapes how players connect, discover communities, and experience matchmaking that feels meaningful and equitable
• Define and lead the modeling architecture for personalization, matchmaking, social graph recommendations, and player/community discovery
• Architect multi-model systems combining skill, preference, trust, and safety signals for fair and meaningful matchmaking
• Develop models for skill inference, player behavior prediction, trust & safety signals, and multi-objective optimization across fairness, latency, and experience quality
• Build and optimize real-time inference systems for personalized content, store offers, matchmaking, and player interactions at global scale
• Drive adoption of advanced modeling approaches including contextual bandits, reinforcement learning, graph ML, and session-aware personalization
• Partner with Data Engineering and Product to shape data schemas, feature pipelines, telemetry standards, and model observability across the ML lifecycle
• Define Responsible AI standards and implement fairness audits, bias mitigation, transparency, and safety mechanisms for matchmaking and social systems
• Lead post-launch evaluations of algorithmic impact on player sentiment, community health, and ecosystem stability
• Set organization-wide standards for model optimization (latency, throughput, memory), multi-model orchestration, and drift detection
• Mentor senior ML engineers and data scientists, influencing system design, experimentation strategy, and modeling craft across organizations
• Represent the ML discipline in cross-functional design reviews, driving alignment on technical decisions, data contracts, and long-term strategy