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About This Role
COMPANY
At 0x, our mission is to "Create a Tokenized World Where All Forms of Value Can Flow Freely". We deliver this to web3 builders with a delightfully simple suite of developer APIs that provide faster trading, better prices, and superior user experience.
Founded in 2017, we’ve processed over 211M\+ transactions and $180B\+ in trading volume from users trading on Matcha, Coinbase Wallet, Robinhood Wallet, Phantom, Rainbow, DefiLlama, MetaMask, Zerion, Zapper, and many more. Our investors include Greylock Partners, Pantera Capital, and Jump Crypto.
When you join 0x, you’ll become part of a veteran team of crypto builders who know the importance of code quality, team cohesion, and a culture of learning.
About the Role
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We are hiring a Data Scientist (GTM) to support our go\-to\-market motion through high\-quality, actionable data.
This role sits at the intersection of on\-chain data and GTM execution, translating complex blockchain activity into insights that directly impact how we prioritize opportunities and close deals.
You will partner closely with GTM, RevOps, and Data teams to:
- Identify where to focus
- Improve deal execution
- Support revenue growth through data
This is a high\-impact individual contributor role focused on applying data to real business outcomes.
What You’ll Do:
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Drive GTM with Data
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- Build frameworks to identify and prioritize opportunities
- Develop scoring approaches to guide GTM focus
- Translate on\-chain activity into clear, actionable insights
- Support GTM decision\-making with data
Support Sales Execution
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- Embed into sales workflows and engagements
- Develop bespoke data narratives to strengthen pitches
- Support live deals with relevant insights
- Use data to differentiate in competitive opportunities
Expand Data Foundations
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- Build on existing data foundations to support GTM needs
- Identify gaps in available data and work with data teams to address them
- Ensure outputs are clear and usable for GTM teams
Translate Data into Action
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- Move from raw data to clear points of view
- Deliver actionable recommendations, not just analysis
- Connect on\-chain signals with business context
Requirements:
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- 4–8\+ years of experience in data or analytics roles
- Experience in Web3 / crypto required
- Strong experience working with on\-chain data (EVM required; Solana a plus)
- Hands\-on experience with tools such as Dune, Allium, or similar platforms
- Strong SQL proficiency
- Ability to go from raw data clear insights actionable recommendations
- Strong data storytelling and communication skills
- Ability to connect data with business outcomes
- Good intuition on what to prioritize and why
- Familiarity with DeFi, DEX aggregators, or on\-chain ecosystems preferred
- AI\-native mindset — leverages tools to increase output and effectiveness
- Willingness to travel globally for customer meetings, conferences, and twice\-annual team offsites.
- Knowledge and passion for decentralized finance and the 0x mission
- Exhibit our core values: do the right thing, consistently ship, and focus on long\-term impact
Nice to Have
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- Experience with CRM systems (e.g., Salesforce, HubSpot)
- Experience with analytics tools (e.g., Amplitude, Heap, FullStory)
Benefits
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- The base salary range for this position is USD $114,000 \- $195\.000 \+ commission (OTE) \+ equity \+ ZRX tokens \+ benefits. Within the range, individual pay is determined by job\-related skills, experience, location, business needs, and candidate preferences between the different compensation elements.
- Comprehensive insurance (medical/dental/vision/life/disability) for U.S.\-based employees — 100% of base plan covered for you and dependents
- 401k and FSA for U.S.\-based employees
- Monthly mobile phone bill, wellness, and pre\-tax transportation expense
- Covered mental health benefits (included professional therapy sessions)
- A supportive remote environment
- Lunch reimbursement for all employees across the globe!
- Stipend for your ideal remote / WFH set\-up: headphones, and any other work gear you may need
- 12\-week paid parental leave
- Great office conveniently located in the SF Financial District for those in the region!
- Flexible vacation: Take time when you need it (and we really mean it!)
0x and its associated entities are dedicated to fostering diversity, inclusion, and belonging in its teams and workforce, and are proud to be equal opportunity employers. 0x does not make employment or hiring decisions on the basis of race, color, creed, religion, sex (including those who are pregnant or have given birth), sexual orientation, gender, gender expression or identity, age, disability, medical condition, genetic information, military or veteran status, marital status, pregnancy, citizenship, national origin, immigration or citizenship status, political affiliation, or any other basis that is protected by applicable local, state, or federal laws. This includes not making such decisions based on the status itself, as well as any associations, perceptions, and assumptions made regarding these statuses. 0x will also consider qualified applicants with arrest and conviction records in a way that is consistent with San Francisco’s Fair Chance Ordinance and similar local laws. Our commitment to equal employment opportunity extends to ensuring that all applicants and employees can perform to their fullest potential, including through obtaining reasonable accommodations when necessary.
Salary Context
This $114K-$195K range is below the median for Data Scientist roles in our dataset (median: $166K across 345 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 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At 0x, 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 $204,700 based on 441 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($154K) sits 25% below the category median. Disclosed range: $114K to $195K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
0x AI Hiring
0x has 1 open AI role right now. They're hiring across Data Scientist. Based in San Francisco, CA, US. Compensation range: $195K - $195K.
Location Context
AI roles in San Francisco pay a median of $244,000 across 1,059 tracked positions. That's 33% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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