Data ScientistLocation: NYC (onsite only - not remote)
We9re hiring a
Data Scientist to join our in-house engineering team. You9ll report directly to Carter (CTO) and will be responsible for owning features from the requirements definition stage to production.
What You9ll Do - Own data science and analytics end-to-end: turn ambiguous questions into analysis, models, internal tools, and production systems without waiting on a PM or a large engineering team.
- Build production Python systems for data collection, enrichment, scoring, and AI-assisted research across internal and external data sources.
- Develop and improve predictive models: define features, build evaluation datasets, run experiments, catch leakage and data-quality issues, and move successful work from research into reliable production releases.
- Write SQL and own reporting including dashboards, recurring reports, one-off investigations, and reconciliation against source data.
- Turn messy data into useful decisions for investing, portfolio support, operations, and growth. The output should be understandable and actionable, not just statistically interesting.
- Work directly with stakeholders to decide what is worth building, explain findings clearly, and iterate based on how the work is actually used.
What we9re looking for - Senior, self-directed data scientist or analytics engineer who can take a loosely defined business problem from first query through a production-quality answer.
- Deep experience with Python and SQL; comfortable working across notebooks, application code, APIs, and Metabase.
- Strong applied modeling judgment: feature design, evaluation, missing data, leakage, calibration, interpretability, and knowing when a simple approach is better.
- Enough data engineering depth to ship your own work: build pipelines, integrate APIs, debug bad source data, and maintain production workflows without heavy engineering support.
- Clear communicator with good product judgment who can work directly with non-technical stakeholders and turn analysis into a recommendation or operating tool.
- Extremely high-agency, entrepreneurial, self-driven.
- Comfortable using modern AI tools and LLMs for analysis, enrichment, evaluation, and automation without treating model output as ground truth.
- NYC-based or willing to relocate (non-negotiable).
Examples of strong qualifications (good to have but not required) - Shipped data products or models that people actually use, with evidence of owning the path from raw data and experimentation through production and iteration.
- Strong public work: a standout GitHub, useful open-source contributions, published research, technical writing, or unusually good independent analysis.
- Experience applying data science to venture, finance, marketplaces, growth, CRM, or other messy operational datasets.
- Experience building LLM evaluation systems, structured extraction pipelines, or AI-assisted research products.
- Founder or early data hire at a fast-moving startup, especially where you operated without a dedicated data platform or large engineering team.
- Clear signals of exceptional quantitative ability: strong research, competition results, Math/Physics Olympiad performance, or a top technical academic background.
Why NOT join us - Not willing to get hands dirty: doesn9t matter how important you were in past organizations; at Alliance we9re all builders, not managers (even though many of us were managers in past lives).
- Prioritizing work/life balance: this role demands focus, hunger, and a career-defining level of commitment. You must be locked in.
- Low agency: if you need someone else to set your priorities or keep you on track, you will fail.
- You can9t relocate to NYC. This is non-negotiable: our founders are here, and so are we.
