The Data Platform team works closely with all teams and cross-functional partners (including product, engineering, and data analysts) to build a foundational data stack powering business analytics.
We are looking for someone to play a mission-critical role in designing and building the machine models for risk, fraud, recommendation, and personalization that powers Bolt. This should be someone with experience, creativity, and passion for producing world-class technology. Companies and consumers alike will rely heavily on what you build, and you’ll have a ton of trust and responsibility. If challenges excite you, and you’re ready for a large one, let us know.
Responsibilities
- Build production ready machine learning models; your models will be the engine that powers all online commerce through Bolt
- Conduct data analysis to determine which policies we adopt and help inform strategic growth
- Build machine learning infrastructure, data pipelines and production ready services to serve live traffic
- Work with other teams at Bolt to engineer new features for models or new product features that help improve Bolt business
Requirements
- Degree in Computer Science, Computer Engineering, or related field
- 2+ years of experience building machine learning models for risk, fraud, recommendation, or similar applications.
- Thorough understanding of machine learning fundamentals and methodologies
- Experience building and deploying machine learning models in an applied setting
- Strong understanding of how to build scalable ML systems supporting online and offline applications
- Experience working with machine learning libraries and frameworks such as scikit-learn, TensorFlow, PyTorch, Spark ML
- Familiarity with best practices of lifecycle management for ML models in industry
- Mastery of a programming language such as Python, Java, Scala
Preferred
- PhD in computer science or related field
- Experience with big data technologies such as BigQuery, Spark, Dataflow, Apache Beam, Pubsub, Cloud Functions, EMR, S3, Glue, Kinesis Firehose, Lambda, etc.
- Industry experience in building large-scale recommendation systems in a consumer-based setting.
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