1 month ago

Sr Applied Scientist

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Last checked: 14 hours agoStatus: Live

Job Summary

Design, develop, and iterate on machine learning models that power core NBA 2K gameplay systems, including matchmaking, skill rating, personalization, and recommender capabilities. Build representation learning models from sequential gameplay data, translate ambiguous product needs into ML tasks, and stay current on state-of-the-art methods. Collaborate with engineers, MLEs, and product stakeholders to integrate models into production systems with scientific rigor.

Location: Dublin

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Seniority: Mid level

Key Responsibilities

•Design, develop, and iterate on machine learning models that impact in-game experiences and core systems such as matchmaking, skill rating systems, and recommender systems.
•Build and refine representation learning models to capture player skill, lineup/build strength, and interaction patterns from sequential gameplay data.
•Stay informed on state-of-the-art techniques, evaluate applicability, and integrate promising methods into internal systems.
•Formulate ambiguous product problems into well-defined machine learning tasks.
•Lead technical discussions, advocate for best practices in model development and deployment, and collaborate with engineers and product stakeholders to integrate models into production systems.

Key Requirements

•Master’s or Ph.D. in a quantitative field (Mathematics, Statistics, Physics, Computer Science, Operations Research, Industrial Engineering, Electrical Engineering).
•Proven experience in applied science, machine learning, or data science roles, including deploying ML models in production.
•Proven experience with deep learning, representation learning, embedding models, or self-/semi-supervised learning.
•Fluency in Python and SQL (or similar scripting languages), with experience using big data technologies such as Apache Spark.
•Working knowledge of ML modeling/computation frameworks such as PyTorch or Tensorflow, plus strong communication skills.

Education:Master's

Skills:CommunicationProblem-solvingIndependenceOwnershipRapid prototyping

Languages:English

Tech Stack:Machine LearningDeep LearningRepresentation LearningEmbeddingsSelf-Supervised LearningSemi-Supervised LearningPythonSQLApache SparkPyTorchTensorflowRNNsTransformersGraph-based ApproachesAutoencodersAWSContainerizationModel OrchestrationModel Serving

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