Job Description

As a Machine Learning Modeler within the Risk Machine Learning and Decision Science team, you work on projects that enable a software driven, machine learning centric view on all money movement and every transaction within the rapidly growing Square ecosystem. This touches on actively maximizing the trade off of revenue growth and risk using artificial intelligence. The machine learning driven software that we release interacts with every transaction and money movement within our seller ecosystem - a profound degree of scale and impact.  Such machine learning techniques touch on transfer learning, reinforcement learning, decision theory, deep learning sequence modeling, natural language processing, and optimization theory.  In addition, we also strive to provide our sellers, through seller facing products, with transparency around why our machine learning made a particular decision.  This touches on algorithms in the relatively new space of explainable artificial intelligence.  

We are seeking a highly motivated and experienced Staff Machine Learning Modeler to join the Payment credit risk team. The successful candidate will lead the development and maintenance of predictive models that drive our credit risk strategies, and will work closely with stakeholders to ensure optimal credit risk management. The successful candidate will play a crucial role in developing and executing the team’s roadmap and long term strategy for payment credit risk modeling, and will be responsible for hands-on building and maintaining predictive ML models that drive our credit risk strategies. You are not only an executor but also a visionary leader to drive business and team growth. 

You will: 

• Build machine learning/deep learning models for payment credit risk assessment that analyze seller / payment activity in real time / batch across Seller’s ecosystem consisting of payments, banking, and debit card products.
• Stay up-to-date with industry trends and advancements in machine learning techniques and share/lead the team for learning/improvement. Adapt existing machine learning methods and transfer learning to develop solutions that work at global scale.
• A senior data / ML leader provides short/long term machine learning strategy, scales the scope of work and team,  making recommendations that lead to e2e execution.
• Leverage an experimentation mindset along with state-of-the-art algorithms to create preventative systems, collaborate on new product features to drive business growth and payment  losses reduction, and explore new datasets (including 3rd party data) to engineer new features for our models. 
• Lead and collaborate with other data scientists, data analysts, subject matter experts, and decision makers to develop success criteria and optimize new products, features, policies, and models

Qualifications

You have:

• An advanced degree (M.S., PhD.), preferably in Computer Science,Engineering, Statistics, Physics, Mathematics or a related technical field.
• PhD plus 4 years (or Master plus 6 years) industry working experience in applied Machine learning or Deep learning
• Familiarity with cloud computing platforms (e.g., AWS, GCP) and big data technologies (e.g., Spark) is a plus
• A strong track record of performing machine learning model development using Python (numpy, pandas, tensorflow, pytorch, scikit-learn, etc.) and SQL/NoSQL interaction patterns.
• Expert level knowledge of modern techniques in machine learning and deep learning, e.g., transformer network architectures, tree models with an orientation to maximizing such algorithms in a large scale production setting. 
• Familiarity with Linux/OS X command line, version control software (git), and general software development principles with a machine learning software development life-cycle orientation.
• Machine learning strategic sequencing of methodological and software improvements to work back from maximizing core metrics associated with optimizing the business.
• The ability to clearly communicate complex results to technical and non-technical audiences and stakeholders (PMs, Operations, Engineers).