Staff Machine Learning Engineer (Modeling), Risk

Key Skills

AWS, Deep Learning, Google Cloud Platform (GCP), Machine learning techniques, NoSQL, NumPy, Python Programming, PyTorch, SQL, TensorFlow

Job Description

Company Description

Since we opened our doors in 2009, the world of commerce has evolved immensely, and so has Square. After enabling anyone to take payments and never miss a sale, we saw sellers stymied by disparate, outmoded products and tools that wouldn’t work together.

To solve this problem, we expanded into software and built integrated solutions to help sellers sell online, manage inventory, book appointments, engage loyal buyers, and hire and pay staff. Across it all, we’ve embedded financial services tools at the point of sale, so merchants can access a business loan and manage their cash flow in one place. Afterpay furthers our goal to provide omnichannel tools that unlock meaningful value and growth, enabling sellers to capture the next generation shopper, increase order sizes, and compete at a larger scale.

Today, we are a partner to sellers of all sizes – large, enterprise-scale businesses with complex operations, sellers just starting, as well as merchants who began selling with Square and have grown larger over time. As our sellers grow, so do our solutions. There is a massive opportunity in front of us. We’re building a significant, meaningful, and lasting business, and we are helping sellers worldwide do the same.

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).

Additional Information

Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.

Zone A: USD $202,500 - USD $247,500

Zone B: USD $192,400 - USD $235,200

Zone C: USD $182,300 - USD $222,800

Zone D: USD $172,200 - USD $210,400

To find a location’s zone designation, please refer to this resource. If a location of interest is not listed, please speak with a recruiter for additional information. 

Full-time employee benefits include the following:

• Healthcare coverage (Medical, Vision and Dental insurance)
• Health Savings Account and Flexible Spending Account
• Retirement Plans including company match 
• Employee Stock Purchase Program
• Wellness programs, including access to mental health, 1:1 financial planners, and a monthly wellness allowance 
• Paid parental and caregiving leave
• Paid time off (including 12 paid holidays)
• Paid sick leave (1 hour per 26 hours worked (max 80 hours per calendar year to the extent legally permissible) for non-exempt employees and covered by our Flexible Time Off policy for exempt employees) 
• Learning and Development resources
• Paid Life insurance, AD&D, and disability benefits 
• Additional Perks such as WFH reimbursements and free access to caregiving, legal, and discounted resources 

These benefits are further detailed in Block's policies. This role is also eligible to participate in Block's equity plan subject to the terms of the applicable plans and policies, and may be eligible for a sign-on bonus. Sales roles may be eligible to participate in a commission plan subject to the terms of the applicable plans and policies. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.

US and Canada EEOC Statement

We’re working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is a proud equal opportunity employer. We work hard to evaluate all employees and job applicants consistently, without regard to race, color, religion, gender, national origin, age, disability, pregnancy, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.