Data Scientist (Mid-Level)

Key Responsibilities

• Identify key challenges and opportunities in our games and data products.
• Define KPIs and metrics to track the success of data initiatives.
• Translate informal requests into formal, structured problem definitions.
• Design appropriate solutions that balance complexity, resources, and time constraints.
• Analyze large-scale datasets to extract actionable insights.
• Collaborate with Data Engineering to develop and deploy impactful data products aligned with our strategic goals.
• Design, implement, and maintain machine learning pipelines and services using ML Ops best practices.
• Apply software engineering principles to build robust, scalable, and maintainable data systems.
• Communicate findings clearly to technical and non-technical stakeholders, fostering a data-driven culture.
• Research and prototype new technologies, creating proof-of-concepts and scalable implementations.

Requirements

• Solid knowledge of statistics, probability, and scientific methodology.
• Hands-on experience with ML Ops (e.g., model versioning, deployment, monitoring).
• Strong software engineering background, including experience with testing, CI/CD, and production systems.
• Working knowledge of core machine learning methods.
• Proficiency in Python and SQL; experience with version control systems (e.g., Git).
• Experience with scientific computing libraries (Jupyter, NumPy, Pandas, SciPy, Matplotlib, scikit-learn).
• Familiarity with cloud platforms such as AWS.
• Excellent communication skills, especially when explaining technical concepts to non-technical audiences.
• Ability to work both independently and within cross-functional teams.
• Passion for games and a proactive approach to continuous learning and improvement.
• Comfortable in a fast-paced, dynamic environment.
• Intellectual curiosity, persistence, and humility.

Nice to Have

• Knowledge of game development, game design, or game programming.
• Experience deploying ML models at scale in production environments.
• Proficiency with statically typed languages (e.g., C++, C#, Java, Scala).
• Understanding of complex systems and distributed architectures.