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Applied Machine Learning Engineer
3 years ago
The Analytics Solutions group within the EA Studios technology organization is seeking a senior-level engineer to work on machine learning software as solutions that are able to apply intuitive models to empower better experience in games’ live service. The ideal team member will be able to think outside of the box and should have real passion for building advanced analytics data products to enable business partners to make sensitive and critical business decisions. Dynamic people, with inspiring and innovative technologies, are the norm here.
Responsibilities
- Use statistical and machine learning techniques to create scalable and end-to-end data science systems.
- Analyzing and understanding large amounts of players data with business use cases to bridge the gaps of data driven cycles.
- Write production-quality code that solves real world problems, in any of our supported algorithm development languages and frameworks.
- Working closely with software engineering teams such as architecture, data engineering, and infrastructure to drive real-time model implementations and new feature creations.
- Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.
- Tracking general business activity and providing clear, compelling management reporting on a regular basis.
- Research and implement novel machine learning and statistical approaches.
Key Qualifications
- A MS in CS, Machine Learning, Statistics or in a highly quantitative field.
- 4+ years of applied ML experience in statistical and mathematical modeling such as supervised and unsupervised machine learning, deep learning, and/or reinforcement learning.
- Passion in backend development experience with a strong interest in work involving data pipelines, distributed systems, performance analysis, and/or large-scale data processing.
- Familiarity with one or more deep learning software frameworks such as Tensorflow, Caffe and PyTorch.
- Proficiency in Python is preferred. We will also consider strong quantitative candidates with a background in other programming languages such as Scala, Java, and C++.
- Familiar with concepts related to testing and maintaining models in production such as A/B testing, retraining, monitoring model performance.
- Experience working in an agile team environment.
Bonus
- Experience with one or more container ecosystems such as Docker and Kubernetes.
- Explored modern data storage, messaging, and processing tools (Kafka, Spark, Hadoop, Cassandra, etc.) and demonstrated experience designing and coding in big-data components such as DynamoDB or similar.
- Experience in CI/CD pipeline tools such as Gitlab and Jenkins.
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