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New York, New York, United States
Hybrid
As humans, there are few things more exciting than meeting someone new. At Tinder, we’re inspired by the challenge of keeping the magic of human connection alive. With tens of millions of users, hundreds of millions of downloads, 2+ billion swipes per day, 20+ million matches per day, and a presence in 190+ countries, our reach is expansive—and rapidly growing.
We work together to solve complex problems. Behind the simplicity of every match, we think deeply about human relationships, behavioral science, network economics, AI and ML, online and real-world safety, cultural nuances, loneliness, love, sex, and more.
The Data Science & Analytics team thrives on data-driven insights to make more informed decisions through our insights into our member's behavior, preferences, and common trends. We take ownership over the integrity of our data and work to improve data literacy across Tinder.
Integrity is where that work meets real-world stakes. Our Integrity organization is built on four product pillars working toward shared outcomes:
Safety Products enhances members' perception of safety through healthy engagement, safe-dating awareness, and support when something goes wrong — owning reporting and the Safety Center, verification and suspension flows, Face Check and Selfie Challenge, and our respectfulness and IRL safety experiences.
Account Integrity secures and qualifies accounts so that each one represents a unique, authentic, qualified individual — while controlling friction, acquisition cost, and privacy. It owns authentication and account access, Passkeys and SMS/OTP, account recovery, and our defenses against account takeover.
Anti-Abuse proactively identifies, disrupts, and mitigates adversarial abuse at scale, balancing accuracy against responsiveness — owning scaled-abuse detection, signal analysis, the rules engine, rapid-response mitigations, and enforcement quality.
Moderation & Ops keeps Tinder safe, resilient, and compliant through the internal tools, back-end systems, and AI integrations that drive moderation quality, operational efficiency, and data hygiene — including appeals and case tooling, AI moderation, and conversation safety systems.
Every decision in this space carries a trade-off: friction that stops a bad actor also costs a real member a moment of their experience, and the only way to navigate that honestly is with rigorous measurement.
We are seeking a Manager, Analytics to lead the data science function supporting Integrity. You will manage a team of three data scientists spanning a range of seniority — with room to grow it as the space expands — while serving as the analytics partner to the Product Team Leads across all four Integrity pillars. This is a hands-on management role: you will own the analytical strategy for the space, but you are also expected to stay close to the data — leading investigations yourself when the question warrants it, and raising the bar on your team's work through direct technical mentorship rather than review alone.
Where you’ll work: This is a hybrid role and requires in-office collaboration three times per week in NYC, Los Angeles, or Dallas.
Lead and Develop a Team of Data Scientists
Manage, coach, and grow a team of data scientists supporting the Integrity organization, owning hiring, career development, and performance.
Set clear ownership across the four pillars and balance your team's capacity against competing roadmap and escalation demands.
Raise analytical quality through hands-on technical mentorship — reviewing methodology, pairing on complex analyses, and unblocking your team on ambiguous problems.
Serve as the Analytics Partner to Integrity Product Leadership
Act as the primary data science partner to the Product Team Leads for Safety Products, Account Integrity, Anti-Abuse, and Moderation & Ops.
Shape roadmaps by sizing opportunities, pressure-testing hypotheses, and defining what success looks like before work starts.
Translate ambiguous safety and abuse problems into tractable analytical questions with clear decision criteria.
Stay Hands-On with the Data
Personally lead high-stakes or highly ambiguous investigations — abuse pattern shifts, enforcement anomalies, unexpected regressions in member experience.
Build and maintain the analytical frameworks the team relies on: prevalence measurement, enforcement precision and recall, appeal and reversal rates, moderation queue throughput and quality.
Retain deep fluency in the underlying data so that your reviews, estimates, and escalations are grounded in the data itself.
Own Measurement and Experimentation for Integrity
Define the metrics that govern Integrity's trade-off between protection and member experience, and hold the organization to them.
Guide experimental design and interpretation for interventions where classic A/B testing is hard — low-prevalence outcomes, adversarial populations, and changes that cannot be ethically withheld.
Establish causal measurement approaches for enforcement actions, verification challenges, and moderation policy changes.
Translate Insights into Executive-Level Narratives
Synthesize complex analyses into clear recommendations for Product, Engineering, Legal, Policy, and executive leadership.
Support regulatory, transparency, and external reporting obligations with defensible numbers and clearly documented methodology.
Influence decisions through structured communication, framing insights in terms of member impact, risk, and trade-offs.
Bachelor’s, Master’s, or equivalent experience in a quantitative field, such as Statistics, Economics, Mathematics, Computer Science, Data Science, or a related discipline.
5+ years of experience in data science or analytics, including 2+ years directly managing data scientists or analysts.
Expert-level SQL and strong working knowledge of Python or R for analysis and modeling.
Deep experience with experimentation and causal inference, including designing approaches for settings where a clean randomized test is not available.
Experience working with large, complex, event-level datasets and modern analytics or business intelligence tools (e.g., Databricks, Tableau, Mode).
Understanding of classifier evaluation, including precision, recall, threshold selection, and the trade-offs between false positives and false negatives.
Experience providing hands-on technical guidance while managing and developing a team.
Experience in Trust & Safety, fraud, risk, content moderation, or another domain involving sensitive or adversarial behavior.
Strong product and business judgment, particularly when balancing member protection, member experience, and operational cost.
Ability to communicate complex findings clearly, influence cross-functional stakeholders, and prioritize across competing needs.
Familiarity with global online safety or age assurance regulations and related reporting obligations.
Experience partnering with machine learning teams on detection or ranking models.
Experience building or adopting AI-assisted analytics workflows at the team level.