Principal Data Scientist - Identity and Matching Models

About Spokeo Spokeo is a people intelligence platform that helps over 18 million monthly visitors reconnect with friends, reunite with families, and build trust in new relationships. Thousands of companies also trust Spokeo's 60 billion public records to improve customer research, help verify information, and prevent fraud. Founded in 2006, Spokeo has built a dedicated, remote-first team with an average tenure of 6.9 years. Comparably has recognized it as a Best Company for Compensation, Employee Happiness, Perks and Benefits, Support for Women, Work-Life Balance, and CEO Leadership. You will build the models that make that call, on messy data from many vendors and public sources, where there is no answer key to check against. You will own a modeling problem from the first question to the production release, and you will see your work change what customers see. The Data Science team is small, so you work directly with the Director of Data Science and the product owners. If you like designing the test as much as building the model, and you want to mentor teammates as the team grows, this is the place to do it. You will choose the approach, train it, test it honestly on held-out data, and ship it.

• This includes using AI models to help label at scale, and checking them against human judgment.
• 15%: Take models to production on AWS, with ML engineering and DevOps. That covers the retrain schedule, the model registry, and a record of where every model and its data came from.
• 10%: Review each data build before it ships. Find what changed, catch regressions, and explain it in plain language to product and sales partners.
• 10%: Document your methods, review teammates' code, and take part in the DS/ML working group.

Python, SQL, Spark, AWS SageMaker, Databricks, and MLflow. The Director of Data Science, product owners, ML engineers, DevOps, and the DS/ML working group. Required: 6+ years of applied data science, though what you have shipped matters more than the count. A Bachelor’s degree in a quantitative field is expected, and a master’s is preferred, or equivalent professional experience. Taken a model from idea to production in a cloud setting (AWS SageMaker, Databricks, MLflow, or similar), monitored it, and measured how it performed after launch. Worked with very large datasets (100 million rows or more) using Spark or a similar tool, with Python and SQL. Told a real improvement from noise, and explained the result, its limits, and the tradeoffs in plain language to engineers and product owners alike. Kept a model healthy as its data changed: knowing when labels and models have gone out of date and need refreshing, and writing the process down so a teammate can pick it up. Handled personal data responsibly, following opt-out, retention, and lineage rules without being reminded. Experience matching or de-duplicating records, using AI to help label data, graph methods, or turning a raw model score into a confidence a product team can act on, is a plus. Our mission is to advance transparency, and to achieve that goal, we rally around six core values: listening with empathy, understanding the why, clarifying with data, innovating to learn, collaborating to achieve, and insisting on quality. As a remote-first company, we can hire team members in the following US states: annual bonus program

• stock options
• 100% medical/dental/vision coverage
• paid home office equipment
• fitness reimbursements
• Applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, or protected veteran status. Spokeo fosters a business culture where ideas and decisions from all people help us grow, innovate, create the best products, and be relevant in a rapidly changing world.

Use of Artificial Intelligence in Our Hiring Process Spokeo may use artificial intelligence-enabled tools to support portions of the recruiting process. These tools may help our recruiting team organize, review, and summarize application materials, candidate experience, and interview information. They may also identify potential inconsistencies or verification signals in application materials for further human review. AI-enabled tools assist our recruiting team but do not replace human judgment. LI-Remote