ABOUT YOU

You have developed ML systems on your own, and are familiar with deploying them in cloud environments. You have solved tough problems across different domains; when challenged with big, new problems you are able to come up with a space of possible solutions, advocate for an approach, and implement seamlessly. You are looking to apply your skills to build new and powerful ML-driven features that will one day be used by digital businesses everywhere. Lastly, you are looking to do this within a small, elite team of product R&D engineers, at a company where we strongly value treating each other well and helping each other succeed.

WHAT YOU'LL BE DOING

• Building the Platform’s Machine Learning Capabilities - We’re building our core ML capabilities from scratch, inspired by open source tools and following basic principles, but tailored to Scuba’s unique behavioral analysis engine. Capabilities we are working on as part of our R&D efforts include in-database AutoML, incremental/online learning, inferencing on data streams, and conversational AI.
• Building New Product Features - We’re building a product unlike any other. Every day we work to solve technical problems in uncharted territory, so that our product can be the best on the market. You’ll be designing, implementing, and maintaining real-time data pipelines and models that enable ML-powered features such as signals discovery, behavioral segmentation, propensity scoring, multi-touch attribution, uplift analysis, and explainability.
• Collaborating - We work with our Engineering, Product, Marketing, Sales, and Customer Success teams to build the best solutions using Scuba’s engine. One of the advantages of being a small startup is that we’re all on a single mission; we have great opportunities to collaborate, share knowledge, and innovate as a team.

SKILLS & KNOWLEDGE

• Bachelor's degree in Economics, Finance, Statistics, Mathematics, Computer Science, Management Information Systems, Engineering, Business Analytics disciplines, or related area; graduate degree preferred.
• 2-6 years of experience working as a Data Scientist / Machine Learning Engineer. Minimum 3 years of experience implementing automated model training and prediction workflows.
• Proficient in Big Data, Python, SQL, TensorFlow and PyTorch. Languages like Go, Rust, Scala, R, and C++ are nice-to-have. Comfortable operating in Linux environments.
• Experience working with data pipelines, real-time systems, and distributed computing.
• Experience building and deploying ML microservices and applications to AWS, Azure and GCP.
• Experience with Large Language Models is great to have.
• Understanding of best practices for machine learning software development, including deployment processes, documentation and coding standards.
• Excellent analytical and problem-solving skills paired with the ability to develop creative and efficient solutions to business problems.
• Ability to lead by envisioning, communicating, influencing and getting things done.
• Experience working in an AGILE development team.