Tech Stack

Description

You will drive the vision, strategy, and execution of data science products at 2K Games, partnering with game studios to enhance player experiences through predictive analytics, personalization, and real-time decision-making. This role blends strategic development, product management, and technical understanding of data science and machine learning to improve game design, live operations, and business intelligence.

Requirements

• 7+ years of experience in product management with a focus on data science, analytics, or ML-driven products
• 5+ years of experience in gaming, technology, or entertainment industries
• Strong understanding of data science methodologies, such as predictive modeling, natural language processing, and computer vision
• Experience with data science tools and platforms, such as Python, SQL, Databricks, TensorFlow, and R
• Customer-focused mentality with focus on the Why and What without getting lost in the How
• Consistent track record of delivering data science products and features from ideation to production, with measurable business impact
• Ability to translate complex data insights into clear product strategies and business opportunities
• Knowledge of real-time analytics platforms, streaming data processing, and feature store management
• Familiarity with data governance frameworks, model risk management, and MLOps best practices preferred
• Advanced degree or equivalent experience in Data Science, Machine Learning, Computer Science, or a related field preferred

Responsibilities

• Develop and implement the product strategy for 2K's central data science platform, partnering with Data Scientists across gaming business units to drive predictive analytics, player personalization, and real-time decision-making.
• Collaborate with the Director of Product, 2K Data to align the data engineering roadmap with customer objectives and player-centric outcomes.
• Define and prioritize a high-impact data science roadmap to lead a team of central data scientists in driving use cases such as player retention modeling, dynamic content generation, and real-time fraud detection.
• Lead the development of reusable and extensible machine learning models, feature engineering pipelines, and ML-driven insights using tools like Databricks, MLflow, and SageMaker.
• Ensure data science initiatives integrate seamlessly with real-time data infrastructure, supporting streaming analytics, AI-powered LiveOps, and dynamic matchmaking systems.
• Establish and maintain self-service tools that enable game teams to experiment with and deploy AI models independently.
• Support the build and enhancement of the central ML feature store.
• Monitor and optimize model performance in production environments, using MLOps best practices.
• Implement governance frameworks to ensure AI models adhere responsibly to data privacy, security, and compliance standards (e.g., CCPA, GDPR).
• Work closely with data scientists across game studios and business units to understand their data science needs and provide data-driven solutions.
• Act as a strategic liaison between data scientists and product teams, ensuring model outputs are actionable, reliable, and aligned with business goals.
• Promote a culture of experimentation and rapid iteration, supporting game studios with A/B testing frameworks, predictive analytics, and personalization engines.
• Identify opportunities to enhance game design and player engagement through advanced analytics and machine learning.