About this opportunity
Tinder lists this Sr. Data Scientist, Recommendations opportunity in palo alto, California. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Join to apply for the Sr. Data Scientist, Recommendations role at Tinder . This role will have a base pay range of $160,000.00/yr - $170,000.00/yr.
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. In 2024, Tinder won four Effie Awards for its first-ever global brand campaign, “It Starts with a Swipe”™.
Our Values
One Team, One Dream – We work hand‑in‑hand, building Tinder for our members.
Own It – We take accountability and strive to make a positive impact in all aspects of our business.
Spark Solutions – We’re problem solvers, focusing on how to best move forward when faced with obstacles.
Embrace Our Differences – We are intentional about building a workplace that reflects the rich diversity of our members.
Team Overview
The Data Science & Analytics team thrives on data‑driven insights to make more informed decisions. 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.
Responsibilities
Collaborate with Product, Engineering, and ML to identify, evaluate, and prioritize new opportunities; frame hypotheses, define success metrics, 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 clarifying options, tradeoffs, risks, and expected business impact.
Serve as a trusted 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.
Qualifications
Bachelor’s, Master’s, or Ph.D. in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Economics).
5+ years of professional experience in data science/analytics at consumer scale, with significant work in recommender systems, ranking, search, or personalization.
Fluency in SQL and Python (required).
Deep understanding of statistics and causal inference; hands‑on experience designing and analyzing online experiments (A/B, variance reduction, sequential testing) and applying quasi‑experimental methods.
Strong product sense and analytical rigor; ability to frame the right questions, choose fit‑for‑purpose methods, and deliver actionable insights with cross‑functional partners.
Familiarity with machine learning for recommendations, including offline/online metric design and model evaluation for ranking/personalization use cases.
Nice to Have
Experience with modern Recs stacks (e.g., retrieval/two‑tower, learning‑to‑rank, embeddings/feature stores) and counterfactual evaluation approaches.
Working knowledge of Spark or similar large‑scale data tools and MLOps concepts (feature stores, evaluation pipelines, drift/monitoring).
Two‑sided marketplace intuition and guardrail design to protect ecosystem health.
Track record of mentorship, thought leadership, and cross‑functional influence.
Benefits
Unlimited PTO (no waiting period) and 10 annual Wellness Days.
Charitable donation matching and volunteer time off.
Comprehensive health, vision, and dental coverage.
401(k) employer match up to 10% and ESPP.
Paid parental leave (including for non‑birthing parents) and Milk Stork shipping.
Professional development stipend and MentorMatch program.
Wellness benefits: Modern Health mental health support, Insight Timer, concierge medical membership, pet insurance, fitness membership subsidy, and commuter subsidy.
Free premium subscriptions for Match Group apps (including Tinder Platinum).
Location & Work Arrangement
This is a hybrid role requiring in‑office collaboration 3 times per week at one of our offices in Los Angeles, Palo Alto, or San Francisco.
Commitment to Inclusion
Tinder is proud to be an equal opportunity workplace where we welcome people of all sexes, gender identities, races, ethnicities, disabilities, and other lived experiences. Even if you do not meet all the listed qualifications, we invite you to apply and show us how your skills could transfer. Learn more about our inclusion efforts .
Disability Accommodations
If you require reasonable accommodation to complete a job application, pre‑employment testing, or a job interview, please speak to your Talent Acquisition Partner directly.
AI Hiring Tools
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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Worksite address
palo alto, CA, 94306, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.