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.

About This Opportunity

Every Spokeo search result depends on one decision: do these records describe the same person, and how sure are we? 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.

What You'll Do:

Rough share of your time, subject to change.

• 35%: Build and improve the models that match records and score how confident we are in each result. You will choose the approach, train it, test it honestly on held-out data, and ship it.

• 20%: Design the labels and the measurement. Decide what to hand-check, build the evaluation sets, and estimate how often we are right when a full answer key does not exist. 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%: Work with the Product Owner to turn findings into stories with clear targets, and defend the priority of the next research spike.

• 10%: Document your methods, review teammates' code, and take part in the DS/ML working group.

Tools. What you will work with: Python, SQL, Spark, AWS SageMaker, Databricks, and MLflow.

Challenges. Noisy and incomplete records, no large hand-labeled set to lean on, and opt-out and retention rules that every model must respect.

People. The Director of Data Science, product owners, ML engineers, DevOps, and the DS/ML working group.

Who You Are:

What you have done matters more than the tools you have touched. Equivalent experience counts.

• Required: 6+ years of applied data science, though what you have shipped matters more than the count.

• Required: 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.

• Judged whether a model is good when no clean answer key exists, for example by sampling and hand-checking results, and said how confident you are in the number.

• 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.

Working at Spokeo

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: AZ, CA, CO, FL, GA, KY, MD, MI, MO, NJ, NV, NC, PA, SC, SD, TX, VA, WA, or WY.

In addition to a highly competitive base salary, our generous benefits include:

• annual bonus program

• stock options

• 401K matching

• 100% medical/dental/vision coverage

• unlimited PTO

• mental health resources

• paid home office equipment

• fitness reimbursements

• support paying for courses

• and more

We extend written offers to candidates who successfully complete their selection process. Offers will depend on several factors, including, but not limited to, marketplace competition, job leveling, experience, and skills.

Privacy Notice for Candidates:

Spokeo is an equal opportunity employer. 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.

Recruiters or staffing agencies: Spokeo is not obligated to compensate any external recruiter or search firm who presents a candidate or their resume or profile to a Spokeo employee without 1) a current, fully executed agreement on file, and 2) being assigned to the open position (as a search) via our applicant tracking solution.

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. Employment decisions are made by people based on a candidate's qualifications and the role's requirements.

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