Sr. Data Scientist, Recommendations

Our Mission

Launched in 2012, Tinder® revolutionized how people meet, growing from 1 match to one billion matches in just two years. This rapid growth demonstrates its ability to fulfill a fundamental human need: real connection. Today, the app has been downloaded over 630 million times, leading to over 97 billion matches, serving approximately 50 million users per month in 190 countries and 45+ languages – a scale unmatched by any other app in the category.

In 2024, Tinder won four Effie Awards for its first‑ever global brand campaign, “It Starts with a Swipe”™.

• One Team, One Dream – build Tinder for our members. We succeed together when we work collaboratively across functions, teams, and time zones, and think outside the box to achieve our company vision and mission.
• Own It – take accountability and strive to make a positive impact in all aspects of our business, through ownership, innovation, and a commitment to excellence.
• Never Stop Learning – cultivate a culture where it’s safe to take risks, seek input, share honest feedback, celebrate wins, and learn from mistakes.
• Spark Solutions – problem solvers focused on moving forward when faced with obstacles: stay agile and overcome hurdles to achieve goals.
• Embrace Our Differences – intentional workplace reflecting diverse members, leveraging different perspectives to build better experiences.

The Data Science & Analytics team thrives on data‑driven insights to make more informed decisions through our understanding of members’ behavior, preferences, and common trends. Recommendations (Recs) is core to Tinder’s experience—covering ranking, retrieval, signals, and model evaluation—to improve match quality, conversations, retention, and revenue through principled ML and experimentation.

As a Senior Data Scientist on the Recommendations team, you will partner closely with Product, Engineering, and Machine Learning to identify and size new opportunities, strengthen existing algorithms, and shape measurement and experimentation across a two‑sided marketplace. You’ll build tooling and dashboards for clear reads, monitor health, deliver executive‑ready presentations, and serve as a trusted partner to the Recs pod while mentoring the broader Data Science team.

This is a hybrid role requiring in‑office collaboration three times per week in Los Angeles, Palo Alto, or San Francisco.

• Work closely with Product, Engineering, and ML to identify and evaluate new opportunities; frame hypotheses, define success metrics and guardrails, and translate findings into clear product recommendations.
• Support the ML team in improving algorithms across retrieval, ranking, and personalization; strengthen offline/online evaluation and alignment.
• Define and lead experimentation design and analysis tailored to a two‑sided marketplace; drive meta‑analyses and playbooks that uplevel reads and decision quality.
• Build tools and dashboards to improve experiment reads and KPI monitoring; standardize templates and health checks for fast, reliable iteration.
• Deliver executive‑ready presentations and docs that clarify options, tradeoffs, risks, and expected business impact.
• Be a trusted and respected partner for the Recs pod, focused on delivering the best recommendations for our worldwide member base.
• Mentor and inspire other data scientists; review analyses and elevate experimentation, causal inference, and model evaluation practices across the team.

• Bachelor’s, Master’s, and/or Ph.D. degree in a quantitative field (Statistics, Mathematics, Computer Science, Economics, or related).
• 5+ years of professional experience in data science/analytics with meaningful time in recommender systems, ranking, search, or personalization at consumer scale.
• Fluency in SQL and Python.
• Deep understanding of statistics and causal inference; hands‑on experience designing and analyzing online experiments and applying quasi‑experimental methods.
• Strong product sense and analytical rigor; ability to frame the right questions, choose fit‑for‑purpose methods, and land actionable recommendations with cross‑functional partners.
• Familiarity with…