Senior Data Scientist

Remitly•Seattle, WA

8d•$176,000 - $220,000•Hybrid

About The Position

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.

Requirements

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, conversion, engagement, and retention across the customer lifecycle.
A track record of partnering effectively with Product, Engineering, and Marketing to influence priorities and decisions.
Clear communication skills, including the ability to distinguish evidence from assumptions and convey uncertainty.
Experience mentoring analysts or data scientists and improving analytical standards across teams.
A degree in a quantitative discipline or equivalent practical experience.
7+ years of experience in Product Analytics, Data Science, Decision Science, or a related analytical role.
Strong expertise in SQL and experience working with large-scale datasets.
Experience designing, executing, and analyzing experiments and A/B tests.
Proven track record influencing product decisions through data-driven insights.
Strong analytical problem-solving and root cause investigation skills.
Experience working closely with Product, Engineering, and Business teams.
Strong understanding of statistical methods, hypothesis testing, and causal inference.
Experience with Analytics and BI / Visualization tools (e.g., Tableau, Mode, Power BI, etc).
Excellent communication and stakeholder management skills.

Nice To Haves

Experience in fintech, financial services, payments, lending, banking, or adjacent regulated industries.
Experience applying Python, R, or other analytical programming languages to predictive analytics use-cases.
Experience building product metrics frameworks and experimentation plans.
Familiarity with data modeling, data warehousing, and modern analytics engineering practices.
Experience measuring and optimizing AI-powered products, features, and/or customer experiences.
Exposure to machine learning and AI applications.
The successful candidate will not only be comfortable using modern AI tools in their day-to-day analytical workflows but will actively seek opportunities to improve decision-making, experimentation, insight generation, and operational efficiency through responsible adoption of AI technologies. AI literacy and the ability to evaluate, implement, and measure AI-driven solutions are increasingly important expectations for this role.

Responsibilities

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.