Addepar is the financial operating system that brings common sense and data-driven investing to our financial world. Addepar gives asset owners and advisors a clearer financial picture at every level, all in one place. It handles all types of assets denominated in any currency. With customizable reporting, financial advisors can visualize and communicate relevant information to anyone who needs it. Secure, scalable, and fast, Addepar is purpose-built to power the global financial system. Hundreds of single and multi-family offices, wealth advisors, large financial institutions, endowments, and foundations manage over $800 billion of assets on the Addepar platform. Addepar has offices in Mountain View, New York City, Salt Lake City, and Chicago.
At its core, Addepar is a data company. We collect position, transaction, and security information from nearly 100 custodial banks, as well as market data from many well known data providers. To this end, Addepar has built an ETL pipeline to integrate all of this data and ship it to clients on a daily basis. You'll be helping to build a small high-impact team that will turn data into insights. You should expect to be challenged with tasks ranging from modeling complex systems, detecting anomalies, building classifiers for equities, to time-series analyses. You will work on the "full stack" of data science from problem definition all the way to delivery to prod.
What you've done:
- Demonstrated experience in solving complex, quantitative problems (a degree in computer science, operations research, mathematics, physics, chemistry, biology, or related field is one way to satisfy this).
- Expertise in SQL and Python or R. The data science team lives in python but we're happy to talk to candidates with other backgrounds.
- Familiarity with modern machine learning techniques and sound grounding in underlying mathematical foundations.
- Ability to work with multiple stakeholders and actively drive projects forward.
- Ability to communicate clearly and concisely in both written and oral modes of communication.
- Expertise or willingness to develop expertise in the intricacies of modern finance.
- Build models of complex systems in order to empower internal stakeholders in making better decisions.
- Develop frameworks to help deliver insights to customers faster.
- Provide support and mentorship to other engineers for their own ML projects.
- Develop tools for internal users. We want problem solvers who can build internal tools to help people detect and triage anomalous data.
- Work with internal teams to develop meaningful metrics to help drive internal decision making.
- Gain deep application-level knowledge of our systems and contribute to their overall design.
At Addepar, we rely on a range of backgrounds, experiences, and ideas. We value diversity, and we’re proud to be an inclusive, equal opportunity workplace.