Machine Learning Scientist
10 days ago
Who We Are
2K Games Dublin is a hub passionate about performance marketing, commercial strategy, data engineering and data science/analytics and is part of the 2K Games group of companies.
Founded in 2005, the 2K label includes some of the most hard-working game development studios in the world today including Firaxis Games, Visual Concepts, Hangar 13, 2K Czech, and Cat Daddy Games. Our world-class team of engineers, developers, graphic artists, and publishing professionals are stewards of a growing library of critically acclaimed franchises such as Battleborn, BioShock, Borderlands, The Darkness, Mafia, NBA 2K, Sid Meier’s Civilization, WWE 2K, and XCOM. 2K is headquartered in Novato, California, and is a wholly-owned label of Take-Two Interactive Software, Inc. (NASDAQ: TTWO).
2K develops and publishes interactive entertainment globally for console systems, handheld gaming systems, and personal computers, including smartphones and tablets, which are delivered through physical retail, digital download, online platforms, and cloud streaming services. 2K publishes titles in today’s most popular gaming genres, including shooters, action, role-playing, strategy, sports, casual, and family entertainment.
What We Need
2K is looking for a hard-working and strategic machine learning scientist to join a rapidly growing team of innovation in the central Data & Analytics Organization. This team brings AI, machine learning, statistics, operations research, and economics to the design, operation, and optimization of 2K’s games.
What You Will Do
- Develop, implement, and build machine learning algorithms to impact in-game experiences
- Work closely with game development, LiveOps, tech, and data engineering teams to apply machine learning and AI solutions to problems in the captivating world of video gaming, including matchmaking, in-game experience personalization, in-game economy, content, and fraud
- Use quantitative models and optimization techniques to support decisions in game design, content release, and LiveOps
- Collaborate with engineers on integrating machine learning solutions to the products.
Who We Think Will Be A Great Fit
The ideal candidate will have a solid foundation in probability, statistics, and machine learning. A passionate learner, quick to pick up new techniques, ready to survey state-of-art academic work or industrial practices and implement viable solutions, eager to broaden their field of knowledge and expertise. Being a creative problem solver, a self-starter with the passion and enthusiasm to get results for meaningful change, meet deadlines, and build whatever is vital along the way is essential for this role.
- Master’s degree in Mathematics, Statistics, Physics, Computer Science, Operations Research, Industrial Engineering, Electrical Engineering.
- 2+ years of experience in machine learning, data science, or applied scientist roles. Fresh Ph.D. with relevant academic experience counts towards this requirement.
- Proficient in machine learning, either Traditional ML or Deep Learning, preferably both.
- Proficient in programming languages popular for data science such as Python, R, Scala. (at least one), or Proficiency in Matlab plus adequate experience (such as coursework) with Python or R.
- Solid understanding of machine learning modeling/computation frameworks such as scikit-learn, PyTorch, Tensorflow, Spark ML
- Experience with relational databases and SQL.
- Proven ability to work independently, rapidly prototyping and testing new insights
- Good interpersonal skills to present, explain, and articulate works to technical colleagues. Comfortable communicating analytical findings and insights with a non-technical audience.
- Technically not a "skill", but, you are comfortable with working hours 10am-6:30pm enabling greater connection with our US HQ.
- Experience with topics such as matchmaking, recommender systems, simulations, or reinforcement learning
- Experience with cloud services such as AWS. Knowledge in containerization, container orchestration, serverless, and model serving.
- Experience with the video gaming industry, or familiarity with video games.