Manager, Analytics (Trust & Safety)

Written by the T&S Network from Match Group's posting.

Leads the data science function behind Tinder's Integrity organization, which covers safety products, account integrity, anti-abuse, and moderation and operations. The work centers on measuring the trade-off between member protection and member experience across abuse detection, enforcement, verification, and moderation. The role manages a team of data scientists and serves as the analytics partner to the product leads for all four Integrity pillars.

• Fraud and scams

What you'd do

• Manage, coach, and grow a team of three data scientists, owning hiring, career development, and performance.
• Assign the team's ownership across the four Integrity pillars and balance capacity against roadmap and escalation demands.
• Review methodology, pair on complex analyses, and unblock the team on ambiguous problems.
• Size opportunities and pressure-test hypotheses with the Integrity product team leads before work starts.
• Personally lead investigations into abuse pattern shifts, enforcement anomalies, and member experience regressions.
• Build frameworks for prevalence, enforcement precision and recall, appeal reversal rates, and moderation queue quality.
• Define the metrics for the protection versus member experience trade-off and design causal measurement for enforcement and policy changes.

What it asks for

• Degree or equivalent experience in a quantitative field like statistics, economics, math, computer science, or data science.
• Five-plus years in analytics or data science, with two-plus years managing analysts or data scientists.
• Top-tier SQL and solid working skills in Python or R.
• Serious causal inference and experimentation background for settings where clean randomized tests aren't possible.
• Working grasp of classifier metrics and threshold tuning, weighing false positives against false negatives.
• Prior work in trust and safety, fraud, risk, moderation, or other adversarial or sensitive settings.
• Knowledge of online safety or age assurance rules and their reporting duties (preferred).

Terms in this role

Prevalence.

How often people see violating content, usually counted as views of it per 10,000 views. A common way to measure enforcement.

Age assurance.

Ways of checking or estimating a user's age, from asking them to facial age estimation and ID checks.

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