Senior Data Scientist

At Remitly, we believe everyone deserves the freedom to access, move, and manage their money wherever life takes them. Since 2011, we've tirelessly delivered on our promise to customers sending money globally, providing secure, simple, and reliable ways to manage their money, ensuring true peace of mind. Whether it's supporting loved ones back home, growing a business across continents, or pursuing new opportunities abroad, we're not just here to move money— we're here to move our global customers forward.

We're looking for builders, reimaginers, and global thinkers who want to work at the intersection of technology, trust, and transformation. If that's you and you're ready to do the most meaningful work of your career—we invite you to join over 2,800 passionate Remitlians worldwide who are united by our vision to transform lives with trusted financial services that transcend borders.

Remitly is looking for a seasoned analytical and product-oriented Senior Data Scientist & Product Analyst to help shape the future of our customer experiences and product strategy for the High Value Sender Customer Segment. This role sits at the intersection of marketing, product, engineering, and data science. In this role, you will identify opportunities to improve customer experiences, design experiments that inform product decisions, and connect marketing activities with downstream customer behavior.

You will be responsible for investigating product performance, identifying root causes behind key business outcomes, and influencing product roadmaps. You will also strengthen experimentation practices across the broader analytics community, be a valuable contributor towards strengthening the data foundation, and help teams make confident decisions grounded in evidence. This is an opportunity for someone who enjoys moving between strategic thinking and hands-on analysis while helping establish a strong analytics culture in a growing fintech organization.

• Discover opportunities to improve our products
• Analyze customer journeys, behavioral patterns, and funnel performance
• Uncover unmet customer needs and opportunities to improve activation, conversion, engagement, and retention.
• Translate findings into prioritized recommendations that influence the product roadmap.
• Partner with Product and Engineering on experimentation
• Turn product hypotheses into well-designed experiments plans.
• Defining success metrics, guardrails, sample sizes, and measurement plans.
• Guide instrumentation, evaluate results, and recommend whether to launch, iterate, or stop.
• Advance experimentation rigor and culture
• Develop reusable standards for experiment design, analysis, and decision-making.
• Address challenges such as selection bias, multiple testing, and heterogeneous treatment effects.
• Mentor analysts and build shared practices through training, peer reviews, and practical tools.
• Connect marketing activities with product experiences
• Examine how acquisition channels, campaigns, targeting, and messaging influence customer intent and subsequent product behavior.
• Identify where marketing promises and product experiences align or diverge, and recommend improvements across the customer lifecycle.
• Apply statistical methods to consequential business questions
• Use causal inference, statistical modeling, and segmentation to understand drivers of customer behavior
• Estimate the impact of interventions when randomized experiments are impractical.
• Partner with Engineering and Analytics Engineering to improve instrumentation, data quality, and metric definitions, making product and growth measurement more reliable and reusable.
• Communicate insights and influence decisions
• Explain findings, uncertainty, and tradeoffs clearly to technical and business audiences.
• Help stakeholders turn analytical evidence into concrete product and investment decisions.

• Demonstrated experience in product data science or analytics, independently leading complex work from problem definition through recommendation and measured impact.
• Strong expertise in experimental design, hypothesis testing, statistical inference, and causal inference, including their practical limitations.
• Advanced SQL skills and proficiency in Python or R for analysis and statistical modeling.
• Strong product judgment and customer curiosity, with the ability to identify valuable opportunities beyond an initial stakeholder request.
• Experience analyzing acquisition, activation,…