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About This Role
Location: New York, United States (Office) \- Must be able to work office based in NYC
On\-Site \| Full\-time
Compensation: $250K \- $350K
We are hiring on behalf of our client, an industry\-leading accelerator program focusing on the intersection of blockchain, cryptocurrency, and artificial intelligence is seeking a highly autonomous and proactive Data Scientist to join its core engineering group. Having backed over 300 high\-growth startups now collectively valued in the tens of billions, our client provides an exceptional launchpad for high\-impact innovation.
In this role, the candidate will report directly to the Chief Technology Officer and take full end\-to\-end ownership of data products—from defining initial requirements to deploying reliable production systems. This is an entirely onsite role based in New York City (NYC) designed for an ambitious, self\-directed builder who thrives in a fast\-paced, high\-agency environment without requiring extensive product management or dedicated platform engineering support.
Key Responsibilities
- End\-to\-End Ownership: Translate complex, ambiguous questions into rigorous analyses, predictive models, internal tooling, and production systems independently.
- Production Engineering: Architect and deploy Python\-based production workflows for automated data collection, enrichment, entity scoring, and AI\-assisted research across disparate internal and external datasets.
- Predictive Modeling \& Experiments: Build, evaluate, and refine predictive models by engineering features, establishing evaluation benchmarks, detecting data leakage, and transitioning research concepts into production\-grade releases.
- Data Architecture \& Reporting: Write optimized SQL queries and maintain analytics infrastructure, including Metabase dashboards, recurring performance reports, ad\-hoc exploratory investigations, and source data reconciliation.
- Strategic Decision Support: Transform raw, complex operational data into actionable strategic insights to support investment strategies, portfolio company operations, ecosystem growth, and internal workflows.
- Stakeholder Collaboration: Partner directly with internal cross\-functional stakeholders to identify high\-value problems, clearly communicate analytical findings, and continuously iterate based on operational usage.
Requirements Basic Qualifications
- Professional Expertise: Proven track record as a Senior Data Scientist or Analytics Engineer capable of driving loosely defined business problems from raw data queries to production solutions.
- Technical Proficiency: Deep expertise in Python and SQL, with comfortable fluency navigating notebooks, application code bases, REST APIs, and business intelligence platforms like Metabase.
- Applied Modeling Judgment: Strong technical discernment regarding feature design, model evaluation metrics, handling missing data, mitigating leakage, and choosing simple, robust approaches when appropriate.
- Data Engineering Competence: Hands\-on ability to build and maintain data pipelines, integrate external APIs, debug inconsistent source datasets, and manage production workflows autonomously.
- Modern AI Tooling: Practical experience leveraging large language models (LLMs) and advanced AI tools for data analysis, enrichment, and workflow automation, paired with a critical approach to verification.
- Communication \& Agency: Outstanding written and verbal communication skills; highly entrepreneurial with exceptional agency and problem\-solving drive.
- Location: Must be currently based in or fully willing to relocate to New York City (onsite requirement is non\-negotiable).
Preferred Qualifications
- Prior experience shipping functional data products or deployed models, demonstrating full lifecycle ownership from raw experimentation through production iteration.
- A strong public portfolio of work, such as a prominent GitHub profile, open\-source contributions, technical publications, or exceptional independent analyses.
- Experience applying data science methodology to venture capital, finance, digital marketplaces, growth analytics, or CRM operational datasets.
- Background in developing structured extraction pipelines, LLM evaluation frameworks, or AI\-assisted research tools.
- Former founder experience or early data hire experience at a fast\-growing startup operating without a dedicated data platform team.
- Strong quantitative signals, such as advanced academic backgrounds in STEM fields (Math, Physics, Computer Science), competition accolades, or published quantitative research.
Benefits
- High\-Impact Network: Direct exposure to top\-tier founders and executives driving innovation across the AI and Web3 ecosystems.
- Exceptional Team Environment: Work alongside a elite team of seasoned builders, technologists, and former founders with backgrounds from leading technology firms and venture ecosystems.
- Direct Visibility \& Ownership: High autonomy with minimal bureaucracy, providing a direct platform to shape organizational strategy and core capabilities.
- Career Acceleration: Comprehensive access to an elite ecosystem offering unparalleled preparation for future entrepreneurial or executive leadership roles.
Interview Process
- Stage 1: Hiring Manager Interview
- Stage 2: Technical Assessment / Interview
- Stage 3: Executive Interview
- Stage 4: Final Selection Interview
Due to the high volume of applications we anticipate, we regret that we are unable to provide individual feedback to all candidates. If you do not hear back from us within 4 weeks of your application, please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish you the best in your job search.
Commitment to Equality and Accessibility:
At MLabs, we are committed to offer equal opportunities to all candidates. We ensure no discrimination, accessible job adverts, and providing information in accessible formats. Our goal is to foster a diverse, inclusive workplace with equal opportunities for all. If you need any reasonable adjustments during any part of the hiring process or you would like to see the job\-advert in an accessible format please let us know at the earliest opportunity by emailing human\[email protected].
MLabs Ltd collects and processes the personal information you provide such as your contact details, work history, resume, and other relevant data for recruitment purposes only. This information is managed securely in accordance with MLabs Ltd’s Privacy Policy and Information Security Policy, and in compliance with applicable data protection laws. Your data may be shared only with clients and trusted partners where necessary for recruitment purposes. You may request the deletion of your data or withdraw your consent at any time by contacting [email protected].
Salary Context
This $250K-$350K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At mLabs, 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($300K) sits 56% above the category median. Disclosed range: $250K to $350K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
mLabs AI Hiring
mLabs has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in New York, NY, US. Compensation range: $350K - $375K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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