Data Scientist

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., * 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., Senior Data Scientist Position Summary Works closely with multi-disciplinary teams, including intuitional leaders and other key stakeholders in the development and implementati…
• 14 hours ago, Data Scientist Position Summary The Data Scientist builds, validates, and supports the deployment of models for defined business problems. This role works closely with stakehol…
• 16 hours ago +

• 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)., * 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.

• 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