Senior Data Scientist

Remitly•Seattle, WA

1d•$129,709 - $192,000•Remote

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. About the Role: Under the supervision of the Principal Data Scientist, the Senior Data Scientist will design and implement fraud detection and risk scoring models using machine learning and statistical modeling to safeguard customer transactions and reduce loss. Telecommuting is available from anywhere in the United States based on manager approval.

Requirements

Position requires Master’s degree in Data Science, Operations Research, Engineering, Statistics, or a related STEM field, and 2 years of experience with data analysis and machine learning.
2 years of experience in machine learning techniques using PyTorch and TensorFlow
2 years of experience in time-series data, multivariate analysis, and causal inference
2 years of experience in statistical experimentation methods using A/B testing, experimental design, power analysis, and non-parametric statistics
2 years of experience in Python, R and SQL.
1 year of experience in applying statistical and econometric modeling techniques to solve business problems
1 year of experience in Airflow, Bayesian Statistics and inference, Git, Keras, PySpark, Shiny, and Tableau.

Responsibilities

Conduct causal inference studies using techniques such as difference-in-differences, instrumental variables, and propensity score matching to evaluate the impact of decisioning systems on customer and business metrics.
Develop dashboards in partnership with analysts and business managers to communicate performance metrics and data analysis across markets and channels.
Identify advanced analytics solutions for measuring efficiency and return on investment for all channels.
Design and propose data-driven solutions to provide insights and recommendations for marketing and fraud teams.
Apply machine learning algorithms, statistical and econometric modeling, and current analytical research to develop at-scale solutions for measurement, hypothesis testing, and optimization.
Implement data models and present technical results to cross-functional stakeholders and non-technical audiences.