Senior Data Scientist, Attribution

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.
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.
Job Description You will: Spearhead the effort to architect and build the next-generation, data-driven attribution system for Square.
This attribution system is a top priority of Square’s GTM effort to optimize investment and drive growth, and will be based on solid scientific methodology that is defensible and explainable Build various attribution models as the building blocks for the next-generation attribution system by utilizing techniques such as MTA (multi-touch attribution), MMM (Marketing Mix Modeling), Incrementality test as well as other emerging technologies Collaborate with MLEs to develop and evaluate advanced MTA by utilizing a combination of first party and third party data Work with data scientists across a wide organization such as marketing, sales, product to design and evaluate attribution models Partner with data engineers to evaluate, improve and potentially revamp existing data pipelines to achieve higher data quality, lower latency and be ready for the next-generation attribution system Take an econometric, holistic view to design and evaluate ROI and marginal ROI metrics that will go hand-in-hand with attribution system to guide Square’s investment to generate best return and fuel growth Collaborate with cross-functional partners.
To name a few: finance, marketing, sales, product, seller lifecycle.
Research and explore cutting edge methodologies, stay up to date with the latest industry trends and best practices Qualifications Deep expertise in statistical and machine learning methods for go-to-market attribution and hands-on experience with building end-to-end attribution systems that may include Marketing Mix Modeling (MMM) algorithmic multi-touch attribution (MTA) 6+ years of data science experience Thorough understanding of economics in go-to-market including but not limited to incrementality, ROI, marginal ROI, elasticity Expert in variety of go-to-market experimentation techniques Experience in full-funnel marketing: metrics set up, performance evaluation and optimization Advanced proficiency in programming languages such as Python, R, SQL Strong ability to coordinate needs from product, finance, go-to-market to form data and data science strategy and align across the organization Track record of working effectively and efficiently with product partners to plan, deliver and evaluate martech product Demonstrated ability to clean and preprocess data using tools like Pandas, NumPy Proven ability to mentor and coach more junior data scientists Strong communication skills and ability to collaborate with cross-functional teams and proven record to influence business strategy Even better: Knowledge of the advertising industry and familiarity with ad tech platforms, including future trends due to evolution of the data privacy landscape Familiarity with classical statistical modeling techniques suitable for small data Expert in causal inference through both testing and modeling An advanced degree (M.
S.
or Ph.
D.
) in Statistics, Computer Science, Operations Research, or a related technical field 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 $184,100 - USD $225,000 Zone B: USD $174,900 - USD $213,700 Zone C: USD $165,700 - USD $202,500 Zone D: USD $156,400 - USD $191,200 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? Additionally, we consider qualified applicants with criminal histories for employment on our team, and always assess candidates on an individualized basis.
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.