Algorithm Research Intern
6 months ago
Responsibilities:
Level Infinite is a global game publisher offering a comprehensive network of bespoke services for games, development teams and studios around the world.We are dedicated to delivering engaging and original gaming experiences to a worldwide audience, whenever and wherever they choose to play, while building a community that fosters inclusivity, connection, and accessibility. The brand also provides a wide range of services and resources to our network of developers and partner studios around the world to help them unlock the true potential of their games. Level Infinite is both publisher of breakout hit games like PUBG MOBILE and Goddess of Victory: NIKKE, and a collaborative partner in games such as Fatshark’s Warhammer 40,008: Darktide, Dune: Awakening from Funcom, Nightingale from Inflexion Studios and many more. To learn more about Level Infinite, visit , and follow on Twitter, Facebook, Instagram and YouTube. - Develop machine learning models for user churn prediction and LTV forecasting, employing advanced statistical techniques and algorithms.- Perform user segmentation to identify distinct groups within our user base, aiding in targeted marketing strategies and personalized user experiences.- Engage in feature engineering to enhance model performance, including selecting, modifying, and creating new features that improve predictive models.- Utilize sequence modeling techniques to analyze time-series data, identifying trends and patterns that inform strategic business decisions.
Requirements:
- Currently pursuing or holding a Master's or PhD degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.- Strong theoretical understanding of machine learning, statistical analysis, and data modeling.- Experience with programming languages such as Python or R, and familiarity with machine learning libraries (, scikit-learn, TensorFlow, PyTorch).- Ability to handle, process, and analyze large datasets, with experience in SQL or similar database query languages.-
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