Data Scientist

Sway•San Francisco, CA

4d•Onsite

About The Position

We're building a tech platform to modernize representative democracy: turning social support into real political leverage. Instead of top-down campaigns, we're creating a viral, decentralized system where leadership flows through networks of trust. We have an existing product (sway.co -- check it out!) that does what no other product on the market can do: allow socially influential leaders to prove their electoral leverage to specific elected representatives and compel politicians to act. Now we're moving into the 2026 midterms with a strong need for a fast, stable, and scalable platform.

Requirements

Live in (or are ready to move to) San Francisco.
Are excited to work with a small, mission-driven team.
Our team works closely in-person 5 days a week—please only apply if this is feasible for you!
Have experience with data analysis, statistical modeling, and machine learning using Python and SQL.
Possess a strong mathematical and statistical modeling background, with the ability to build rigorous, custom models from scratch.
Have experience building and shipping data products or models quickly but robustly, ideally in an early-stage startup context.
Hold a Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Mathematics, or previous equivalent experience.
Are extremely motivated to learn and grow.
Great communicator and team player -- our engineering team interfaces regularly with users to get feedback and drive product.
Are passionate about working in a high-impact, government-related problem space.

Nice To Haves

You’ve built a large project from scratch or started something yourself.
Experience working with social graphs, network analysis, or consumer growth strategy.

Responsibilities

Analyze and model our social graph to map networks of trust and accurately quantify electoral leverage.
Design and deploy statistical models to enhance our user verification systems, ensuring the integrity of the network.
Develop algorithms to identify viral growth patterns and optimize our distribution strategies.
Collaborate directly with our engineering, product, and distribution teams to define core metrics, design rigorous experiments (A/B tests), and rapidly iterate on new ideas.
Build out core data pipelines and dashboards using a modern data stack (Python, SQL, and relevant ML/analytics infrastructure) to empower the team with real-time insights.