Software Engineer - Machine Learning Engineer (Intern)

SF Bay Area /
Engineering /
Full-time
At Sisu, we're building a software platform that empowers people to make better decisions using data. Based on years of cutting-edge research at Stanford, Sisu enables users to quickly and comprehensively understand what’s driving their key metrics, so they never miss a window of opportunity to act.

Sisu is a fundamental rearchitecture of how users interact with data. Instead of relying on users to tell us what they want to see, as in traditional dashboarding systems, Sisu predicts users’ intent based on their metrics of interest, and past behaviors. We take this kind of predictive analysis for granted in consumer applications (e.g., Netflix, Google, Facebook), but the hardest interface design and ranking problems lie in making private data found in everyday organizations actionable. The key problem we’re solving as a first step in this direction is to help identify what’s driving change in key metrics like revenue, retention, and churn among the enormous set of factors like user demographics, campaigns, and acquisition channels. To do so, we combine statistical analysis and machine learning at scale to provide users personalized, real-time diagnoses of changes in their metrics via an explainable, interpretable user interface.

As an intern in our Machine Learning team, you’ll have the opportunity to shape the future of machine learning at Sisu. Much like public search engines rely on sophisticated algorithms for ranking and relevance over unstructured text, machine learning lies at the core of Sisu's value proposition, driving functionality across our stack from data preprocessing to the surfacing and ranking of insights.

Preferred Qualifications

    • Currently enrolled in a full-time, degree-seeking program and in the process of obtaining a Bachelors or Masters degree
    • Proficiency with Python and experience with data analytics tools (e.g., scipy, numpy, pandas)
    • Interest in data analytics, statistics, and machine learning for tabular data
    • Strong math and CS fundamentals including probability theory, algorithms, and data structures.