Data Scientist, Square Point of Sale

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, 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.
The Role
As a member of the Square Point of Sale Data Science team, you will use statistical, machine learning, analytics, and data engineering techniques to empower decision-making for Square's new unified point of sale product. Over the past year, one of Square's top strategic bets has been to converge a disparate set of POS apps into a single one that can serve a large and diverse set of sellers, in turn delivering tremendous business value. As this team continues to scale and build, there will be numerous ways for data science to generate additional impact, such as strategic sizing of opportunities, data foundations/instrumentation, operationalizing new metrics, evaluating the incremental impact of new launches, leveraging machine learning to carry out segmentation/generate predictive insights, and collaborating with other orgs across Square.
You Will

• Partner with the Square Point of Sale team to make data-informed decisions that have significant impact;
• Apply a diverse set of techniques including statistical analysis, machine learning (ML), analytics, business intelligence (dashboarding), and data engineering to generate strategic insights;
• Collaborate with the broader Square Data org to drive seller value and overall technical excellence;
• Communicate analysis and recommendations to high-level business partners in verbal, visual, and written forms;
• Develop resources and collaboration processes to empower data access and self-service so that your expertise can be leveraged where it is most impactful.

You Have

• Bachelor's degree or equivalent in a quantitative discipline;
• 3+ years of data science experience or equivalent;
• Fluency with Python and SQL;
• Solid statistical foundations (e.g. A/B testing);
• Familiarity with standard machine learning concepts (e.g. regression/classification, clustering, offline/online model evaluation) and curiosity to learn more modern techniques as required;
• Familiarity with data engineering best practices;
• Experience leading cross-functional projects and partnering with Product/Marketing/Design/Engineering/UX Research on strategy and prioritization;
• Excellent verbal and written communication.
• Experience working in the payments or SaaS domain (Nice to have).

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 an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We also consider qualified applicants with criminal histories for employment on our team, and always assess candidates on an individualized basis.
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 .
Block will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and "fair chance" ordinances.
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.
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.
Zone A:
$142,000 - $213,000 USD
Zone B:
$134,900 - $202,300 USD
Zone C:
$127,800 - $191,600 USD
Zone D:
$120,700 - $181,100 USD
Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Check out benefits at Block.
Block, Inc. (NYSE: SQ) is a global technology company with a focus on financial services. Made up of Square, Cash App, Spiral, TIDAL, and TBD, we build tools to help more people access the economy. Square helps sellers run and grow their businesses with its integrated ecosystem of commerce solutions, business software, and banking services. With Cash App, anyone can easily send, spend, or invest their money in stocks or Bitcoin. Spiral (formerly Square Crypto) builds and funds free, open-source Bitcoin projects. Artists use TIDAL to help them succeed as entrepreneurs and connect more deeply with fans. TBD is building an open developer platform to make it easier to access Bitcoin and other blockchain technologies without having to go through an institution.