Data Scientist, Square Support

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

Our innovative and technology-forward approach to risk management and customer support has been fundamental to how we invent and build financial products. The Risk team at Block continues this legacy through a sophisticated, technology and science-led approach to protecting our customers and their accounts. Our interdisciplinary structure in Risk combines Product Development, Science teams (specializing in modeling, analytics, and data science), Operations and key partners including Legal, Compliance and Policy teams, all working in concert to identify, assess, and solve complex risk challenges across fraud prevention, support, and compliance.

In this role, you'll be embedded within the Support Data Science team, collaborating with cross-functional partners to drive strategy through advanced statistical techniques with a particular focus on serving our Square customer base. This is an exciting opportunity to both build upon existing frameworks and pioneer new solutions in our merchant support space. You'll work closely with our applied machine learning and product teams to experiment, measure, and implement data-driven improvements to how Square customers receive help. Our customer base presents unique analytical challenges due to its diverse composition of business types and varying support needs. You'll be instrumental in developing and maintaining foundational metrics that guide our support strategy, while continuously experimenting to identify what approaches work best for different customer segments. As part of a team that's actively building its analytical foundation, you'll have the chance to shape how we measure, understand, and enhance the customer support journey.

You Will

• Analyze large datasets using SQL and scripting languages to surface actionable insights and opportunities to the product team and other key stakeholders
• Approach problems from first principles, using a variety of statistical and mathematical modeling techniques to research and understand customer behavior
• Design and analyze A/B experiments as well as pseudo-experimental techniques like causal inference and difference-in-difference
• Work with data engineers to log new, useful data sources to reflect our product features and with ML engineers to develop models and productionalize them
• Build, visualize and report on metrics that drive strategy and facilitate decision making for key business initiatives
• Write code to effectively process, cleanse, and combine data sources in the pursuit of insights and exploratory analytical endeavours 
• Partner closely with business intelligence and data engineering teams to build out curated ETL datasets that are easily used by the broader team
• Effectively communicate your work with team leads and cross-functional stakeholders on a regular basis
• Identify and develop automation opportunities in our current processes / workflows

We're Targeting

• A Level 5 hire - typical experience for L5 would be something like BSc with 4-6 years; MSc with 2-5 years; or a PhD with 1-3 years 
• A background in Statistics, Mathematics, Biostatistics, Economics or related quantitative field
• Advanced proficiency with SQL and data visualization tools (e.g. Tableau, Looker, etc)
• Extensive experience with scripting and data analysis programming languages, such as Python or R
• Strong experience with experimentation techniques
• Deep familiarity with cohort and funnel analyses, a well-developed understanding statistical concepts such as selection bias, probability distributions, and conditional probabilities
• Passion for understanding human behavior and improving products/features that real customers use

Technologies We Use and Teach

• SQL, Snowflake, etc.
• Python (Pandas, Numpy)
• Looker, Databricks, Airflow, Mode, Prefect, Tableau

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 will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and "fair chance" ordinances.

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.

While there is no specific deadline to apply for this role, U.S. roles are typically open for an average of 55 days before being filled by a successful candidate. Please refer to the date listed at the top of this job page for when this role was first posted.