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