The Consumer Platform ML team’s mission is to deliver state of the art ML algorithms that will help build robust and scalable customer products. We use a data-driven approach to solve the problems and deliver ML-powered smart user experiences on the consumer apps and website. This team works in close partnership with our product managers, application and website teams to deeply understand the problems and deliver the most impactful solutions. We are constantly faced with challenges at industrial scale such as Personalization, Information Retrieval, Relevance Ranking and are pushing the boundary of how Affirm thinks of its data.
We are looking for highly motivated engineers to help build this team in Poland. Come join us!
responsibilities :
Design, develop, and deploy a ML-powered solution for building effective personalized user experiences, search optimization and other challenging problems in customer space
Build tools for our team to enable (1) personalization of content (2) relevance ranking, and (3) better and more effective information retrieval
Partner with Analyst, Data Science, and Product engineering teams to build production machine learning models; your models will decide what, when and to who will be shown in our app and website
Develop our understanding of new data sources and how they may improve our existing processes
Work closely with Affirm’s mobile application, website application, marketing, and analytics teams to understand our performance modeling strategies and the business drivers underlying those strategies
Serve as a trusted advisor on the application and implementation of machine learning across Affirm
requirements-expected :
B.S. with 8+ years of industry experience, M.S. with 7+ years, PhD with 6+ years, or equivalent experience
Demonstrated experience designing real time systems and writing production-quality software
Experience with the AWS technical stack and data infrastructure such as MySQL, Spark, Kubernetes, Docker, and Airflow
Proficiency in machine learning with experience in areas such as gradient boosting, deep learning, recommendation systems, computational advertising, reinforcement learning, financial forecasting, time series analysis, anomaly detection, monte-carlo simulations, and Markov decision processes
Strong programming skills in Python. Experience using frameworks for machine learning and data science like scikit-learn, pandas, numpy, XGBoost, TensorFlow, mllib
Excellent written and oral communication skills including the capability to drive requirements with product and engineering teams and present technical concepts and results in an audience-appropriate way
Ability to work efficiently both solo and as part of a team; willingness to learn new things and mentor others
Passion to change consumer banking for the better, while developing a deeper understanding of applied machine learning
