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About This Role
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
Square's Sales organization is where we're placing our biggest bets, and the go\-to\-market (GTM) Data Science team sits at the heart of that growth engine. Reporting to the Sales Data Science lead, you'll own a mature subdomain of our Sales function and contribute across the full channel, alongside peers in Sales Data Science (DS) and the wider GTM Data Science org. The role is a rare combination: you'll be a trusted analytics leader who sales executives turn to for the ground truth on business performance, and a hands\-on data scientist who solves big, ambiguous problems end to end. This is a DS team on the front lines of AI, one that uses agents daily to clear away the mundane and free up time for the high\-impact, technically challenging work that moves the business forward. You'll grow here: there's no shortage of hard problems, plenty of sharp colleagues to learn from, and the leverage to scale your impact well beyond your own domain.
#### You Will
- Own performance measurement for your subdomain: monitoring the metrics that matter (wins, new revenue, funnel conversion, rep productivity), reporting against plan, and presenting the story in business reviews with sales leadership
- Lead investigations into performance questions ("why is X down?", "how can we improve Y?"), decomposing KPIs from first principles and designing experiments to test what actually works
- Take on large, ambiguous projects autonomously: scoping the problem, finding the right data, thinking critically about what it can and can't tell you, and landing the results with stakeholders
- Own and evolve your subdomain's data foundation, and partner with Sales DS peers on shared metrics, attribution logic, and cross\-cutting analyses
- Translate data into business context: telling the story behind the numbers, pushing back on hypotheses with evidence, and building trust with senior sales, finance, and operations leaders
- Work AI\-natively: use agents throughout your workflow and pioneer new ways of applying AI to the hardest problems in the sales domain
- Build self\-serve dashboards and datasets that let the sales org answer its own questions
- Raise the bar for the team's analytical rigor, communication, and AI\-enabled ways of working
#### You Have
- Minimum of 8 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or a PhD with 3 years experience; or equivalent experience
- Advanced SQL and strong Python; comfort owning the full stack from ETL to analysis to visualization
- Strong statistical foundations: experiment design, causal inference, funnel and cohort analysis, and forecasting; you can interpret results with rigor and explain the concepts behind them
- A track record of running large projects end to end with minimal direction; you're the person leadership trusts when the numbers matter
- Strong critical thinking: you know when to move fast and when to slow down on a problem with big consequences, and you pressure\-test your own conclusions
- Exceptional communication: you can present performance to executives, translate technical nuance for non\-technical partners, and write analysis documents that stand on its own
- Enthusiasm for building with AI, and curiosity about how far it can be pushed in data science work
- Curiosity about unfamiliar data and how sales organizations actually work, and the empathy to partner well with the people on the frontline
Nice to have, but not required:
- Prior experience supporting sales, GTM, or revenue organizations
- Familiarity with fintech, payments, or the SMB merchant space
We value strong fundamentals and curiosity over industry background; we'll teach you our domain.
#### Technologies We Use and Teach
- SQL, Snowflake, Databricks, Python (Pandas, NumPy)
- Looker, Omni, Airflow, dbt\-style ETL patterns
- Agentic AI: agent skills and bots, automated reporting workflows, in\-house tooling
- Salesforce and the modern GTM data stack
- A/B, matched\-market, and causal testing
Pay Transparency:
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: ($198,000 \- $297,000\)
Zone B: ($188,100 \- $282,100\)
Zone C: ($178,200 \- $267,400\)
Zone D: ($168,300\- $252,500\)
Application Guidelines
Candidates may submit up to 9 active applications within a 60\-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed.
Use of AI in Our Hiring Process
We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws.
Contact us here with hiring practice or data usage questions.
*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. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering.*
*Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people.* *Square* *makes commerce and financial services accessible to sellers.* *Cash App* *is the easy way to spend, send, and store money.* *Afterpay* *is transforming the way customers manage their spending over time.* *TIDAL* *is a music platform that empowers artists to thrive as entrepreneurs.* *Bitkey* *is a simple self\-custody wallet built for bitcoin.* *Proto* *is a suite of bitcoin mining products and services. Together, we’re helping build a financial system that is open to everyone.*
Salary Context
This $178K-$297K range is above the 75th percentile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Block, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($237K) sits 23% above the category median. Disclosed range: $178K to $297K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Block AI Hiring
Block has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in San Francisco Bay Area, CA, US. Compensation range: $297K - $343K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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