Interested in this Data Scientist role at Spokeo?
Apply Now →Skills & Technologies
About This Role
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: https://www.spokeo.com/recruiting-policy
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.
#LI-Remote
Salary Context
This $166K-$216K range is above the 75th percentile for Data Scientist roles in our dataset (median: $162K across 195 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Spokeo, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,564 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $166K to $216K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Spokeo AI Hiring
Spokeo has 1 open AI role right now. They're hiring across Data Scientist. Based in Pasadena, CA, US. Compensation range: $216K - $216K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.