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About This Role
About the Company
Born from groundbreaking research at Columbia University and Yale University, CertiK is a leading Web3 security company focused on securing blockchain protocols, smart contracts, and decentralized applications through cutting\-edge security research, formal verification, and AI\-powered technology. Founded in 2017 and headquartered in New York City, CertiK provides end\-to\-end security solutions including smart contract audits, penetration testing, on\-chain monitoring, incident response, and compliance services for some of the largest projects in the digital asset ecosystem.
Today, CertiK supports thousands of enterprise clients and Web3 projects globally, with a distributed international team spanning North America, Asia, and Europe. The company is backed by leading investors including Coatue, Goldman Sachs, Insight Partners, and Sequoia Capital, and has been recognized by organizations such as the World Economic Forum and CB Insights for its contributions to blockchain security innovation.
About the Role
The primary responsibility of this role is to build/maintain ETL pipelines \& process large datasets from APIs/databases/third\-party platforms to enable real\-time team analytics and automate data preprocessing (cleaning/normalization/validation) for client accounts using rule\-based logic/statistical checks to ensure data quality \& prepare analysis\-ready datasets for modeling/reporting.
### Responsibilities
- Analyze large\-scale blockchain/transactional/social\-media datasets to identify patterns/trends/anomalies/risk indicators.
- Develop/apply machine learning models (graph\-based algorithms \& NLP techniques) for threat detection/behavioral analysis/monitoring.
- Perform feature engineering/model training/testing/validation to ensure accuracy/robustness/interpretability.
- Design/implement scalable data pipelines/ETL processes \& CI/CD workflows for ingestion/preprocessing/aggregating blockchain \& social media data.
- Create dashboards/visualizations to deliver actionable insights \& provide data\-driven guidance for strategic planning.
- Collaborate with engineering/product/business teams to translate analytical requirements into scalable data\-science solutions.
### Requirements
- Master’s degree in Data Science, Statistics, or a related field.
- Sound knowledge of feature engineering/model evaluation/validation \& on\-chain patterns/risk\-analysis/threat\-detection methodologies.
- In\-depth understanding of blockchain/distributed ledger data structures \& analytics.
- Strong ability to apply machine\-learning \& statistical modeling techniques to large\-scale datasets.
- Expertise in analyzing graph/text\-based or transactional data.
- Familiar with cloud platforms (AWS/Azure/GCP) \& Spark\-based distributed\-computing systems (e.g., Databricks).
- Proficient in Python, SQL (PostgreSQL/MySQL/NoSQL) \& ETL tools (Apache Airflow).
Target annual salary compensation for this role performed is $110,000 to $125,000\. The exact compensation at which this job is filled will be determined by the skills and experience of qualified candidates.
CertiK is proud to offer medical, vision, and dental insurance, 401(k) plan with company matching, life and accidental death and dismemberment insurance, HSA (with high deductible plan), FSA, and other benefits to all full\-time employees, along with flexible paid time off and holidays. CertiK also offers a variable commission program for business development sales roles.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.
CertiK is proud to be an equal opportunity employer. We will not discriminate against any applicant or employee on the basis of age, race, color, creed, religion, sex, sexual orientation, gender, gender identity or expression, medical condition, national origin, ancestry, citizenship, marital status or civil partnership/union status, physical or mental disability, pregnancy, childbirth, genetic information, military and veteran status, or any other basis prohibited by applicable federal, state or local law.
CertiK will consider for employment qualified applicants with criminal histories in a manner consistent with local and federal requirements.
https://www.eeoc.gov/sites/default/files/migrated\_files/employers/poster\_screen\_reader\_optimized.pdf
All CertiK employees are expected to actively support diversity on their teams, and in the Company.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $110K-$125K range is in the lower quartile for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At CertiK, 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($117K) sits 41% below the category median. Disclosed range: $110K to $125K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
CertiK AI Hiring
CertiK has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $125K - $125K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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