Senior Applied Scientist - NBA 2K

Who We Are

2K is headquartered in Novato, California and is a wholly owned label of Take-Two 

Interactive Software, Inc. (NASDAQ: TTWO). Founded in 2005, 2K Games is a global

video game company, publishing titles developed by some of the most influential

game development studios in the world. Our studios responsible for developing 2K’s

portfolio of world-class games across multiple platforms, include Visual Concepts,

Firaxis, Hangar 13, CatDaddy, Cloud Chamber, 31st Union, HB Studios, and 2K

SportsLab. Our portfolio of titles is expanding due to our global strategic plan, building

and acquiring exciting studios whose content continues to inspire all of us! 2K

publishes titles in today’s most popular gaming genres, including sports, shooters,

action, role-playing, strategy, casual, and family entertainment.

Our team of engineers, marketers, artists, writers, data scientists, producers, thinkers

and doers, are the professional publishing stewards of

2K’s portfolio currently

includes several AAA, sports and entertainment brands, including global

powerhouse NBA®️ 2K, renowned BioShock®️ , Borderlands®️ , Mafia, Sid Meier’s

Civilization®️ and XCOM®️ brands; popular WWE®️ 2K and WWE®️ SuperCard

franchises, TopSpin 2K25, as well as the critically and commercially acclaimed PGA

TOUR®️ 2K.

At 2K, we pride ourselves on creating an inclusive work environment, which means 

encouraging our teams to Come as You Are and do your best work! We encourage ALL

applicants to explore our global positions, even if they don’t meet every requirement

for the role. If you're interested in the job and think you have what it takes to work at

2K, we encourage you to apply!

The Applied AI Team within 2K’s Product Organization is seeking a Sr. Applied Scientist to contribute to the continued innovation behind our flagship title, NBA 2K. This team builds production-grade algorithmic systems that directly shape the player experience. Our work spans core areas such as:

• Matchmaking
• In-game experience personalization
• Player engagement
• In-game economy
• Content optimization
• Fraud detection

We combine techniques from machine learning, statistics, operations research, and economics, and partner closely with engineering, game design, and LiveOps to bring AI-driven decisioning to life across the game.

• Research, build, and deploy advanced machine learning and optimization models to improve gameplay and player engagement.
• Work across areas like matchmaking, ranking systems, personalization, and in-game economy modeling.
• Collaborate with game development, LiveOps, tech and data engineers, and product managers to scope high-impact problems and deliver solutions.
• Lead technical discussions, advocate for best practices in model development and deployment, and ensure scientific rigor.
• Stay informed on state-of-the-art techniques, evaluate their applicability, and lead efforts to integrate promising methods into our systems.

We are seeking a hands-on senior scientist who combines deep technical expertise in machine learning with strong product sense and a track record of delivering high-impact, production-ready solutions. You are comfortable leading projects independently and thrive in a collaborative, fast-moving environment.

Requirements:

• Master’s or Ph.D. in a quantitative field such as Computer Science, Statistics, Applied Math, Physics, Operations Research, or a related discipline.
• 4+ years of experience in applied science, machine learning, or data science roles, including experience deploying ML models in production environments.
• Strong background in machine learning (traditional and/or deep learning), statistical modeling, and experimentation.
• Proficiency in Python and SQL; experience with distributed computing (e.g., Spark).
• Working knowledge of ML frameworks like scikit-learn, PyTorch, TensorFlow, and pipeline tools for model training and deployment.
• Demonstrated ability to own projects end-to-end and deliver measurable business value.
• Excellent communication skills, with the ability to explain complex ideas to both technical and non-technical audiences.

Preferred Qualifications (Helpful, but Not Required):

• Experience with discrete optimization, reinforcement learning, recommender systems, and simulation-based modeling.
• Familiarity with production ML infrastructure, including cloud services (AWS), containerization, and model orchestration/serving.
• Knowledge of the gaming industry and experience working with game-related AI solutions.
• Interest in basketball and a willingness to engage with video games as products.