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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