Square builds common business tools in unconventional ways so more people can start, run, and grow their businesses. When Square started, it was difficult and expensive (or just plain impossible) for some businesses to take credit cards. 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.
So we expanded into software and started building integrated, omnichannel solutions – to help sellers sell online, manage inventory, offer buy now, pay later functionality through Afterpay, 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. We’re working to find new and better ways to help businesses succeed on their own terms—and we’re looking for people like you to help shape tomorrow at Square.
Our Customer Identity and Access Management (CIAM) team sits within the Commerce Platform organization and is responsible for creating remarkable products and experiences for our sellers, developers, buyers, and more. We’re responsible for creating a shared platform for user accounts, access management, and policy enforcement. Growing Upmarket is a critical priority for Square, and this is a pivotal role to enable us to succeed. The team is responsible for foundational data models, APIs, intuitive UX for various actors to set up and manage their accounts within Square, and tooling to enable internal stakeholders to support our sellers better. This includes ownership of standards-based authentication flows and session management across the Square ecosystem for all platforms (iOS, Android, and web).
As a Data Scientist on Square Commerce Platform’s centralized data science team, you will leverage analytics, engineering, and machine learning to empower data-driven decision making, with the aim of ensuring a reliable, scalable, and secure platform for Square and our sellers. You will partner closely with product managers, engineers, and other data scientists to identify gaps and opportunities in our current processes, design business, and product metrics, lead experimentation initiatives to improve those metrics, and develop automated solutions to help partner teams understand the health of our core business.
- Partner primarily with CIAM product, engineering, and design teams to make data-driven decisions using a diverse set of tactics, including statistics, quantitative reasoning, and experimental design. You will also work closely with Risk, Payments, and other cross-functional teams.
- Proactively identify gaps and opportunities in our processes. Design and analyze experiments with the goal of increasing Square’s bottom line and ensuring Sellers' accounts are safe.
- Build ETL pipelines, dashboards, and machine learning models that enable monitoring of the health and performance of our core business.
- Be the subject matter expert on CIAM data. Translate requests from internal and external partner teams into questions that can be rigorously answered with data.
- Communicate analysis and decisions to high-level stakeholders and executives in verbal, visual, and written media.
- Evangelize data best practices and be an advocate for a data-driven culture across Square.
- 2+ years of industry experience in data science, product analytics, or machine learning-focused roles.
- Proficiency in SQL and Python, as well as working knowledge in the application of statistical and modeling techniques; willingness to learn new technologies on the job.
- Proven track record of adding business value using data.
- A strong Data Engineering background. Ability to ingest and analyze large data sources independently.
- Proficiency in at least one visualization tool (we primarily use Looker).
- Strong written and verbal communication skills and the ability to build strong relationships and influence product partners.
- Prior experience in account security and management is beneficial, but not required.
- Comfort working in a dynamic work environment.
Technologies we use and teach:
- Python (numpy, pandas, sklearn)
- Machine Learning (e.g. regression, ensemble methods, neural networks, etc.)
- Google Cloud Platform: BigQuery, Dataflow, and Composer
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 $125,600 - USD $153,600 Zone B: USD $119,300 - USD $145,900 Zone C: USD $113,000 - USD $138,200 Zone D: USD $106,800 - USD $130,600
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.
We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we’re doing to build a workplace that is fair and square? Check out our I+D page.
Additionally, we consider qualified applicants with criminal histories for employment on our team, and always assess candidates on an individualized basis.