Senior AI Data Scientist

Posted Yesterday
Hiring Remotely in USA
Remote or Hybrid
Senior level
Software
The Role
Design and deploy ML systems for enterprise search, recommendations, retrieval, and AI agents. Build scalable data/ML pipelines, evaluation frameworks, and metrics. Apply statistics, experimentation, and causal inference to guide product decisions. Collaborate cross-functionally, prototype LLM-based capabilities (embeddings, RAG), and mentor junior team members.
Summary Generated by Built In
Description

Chaos Labs builds infrastructure for organizations adopting AI at scale. Our products help organizations understand their AI work, capture the knowledge it generates, and turn everyday AI activity into intelligence they own.

Reporting directly to the Chief Science Officer we are looking for a talented Senior AI Data Scientist to join our team and help shape the future of AI.

Responsibilities

  • Design and build machine learning systems that power enterprise search, retrieval, recommendations, and AI agents.
  • Develop evaluation frameworks and metrics to measure the quality, performance, and business impact of LLM-powered applications.
  • Build scalable data and ML pipelines that process and analyze large-scale AI interaction data.
  • Apply statistical modeling, experimentation, and causal inference to guide product development and strategic decision-making.
  • Collaborate closely with Product, Engineering, and Design to translate research and insights into production features.
  • Analyze product usage and customer behavior to identify opportunities for improving AI adoption, productivity, and user experience.
  • Prototype, evaluate, and deploy new AI capabilities using foundation models, embeddings, and retrieval-augmented generation (RAG) techniques.
  • Define key performance indicators and build analytical frameworks that inform product strategy and company-wide decisions.
  • Contribute to the technical direction of our AI platform by identifying new opportunities to leverage machine learning and generative AI.
  • Mentor junior team members and help establish best practices in data science, experimentation, and machine learning across the organization.
Requirements
  • 5+ years of experience in Data Science, Applied Machine Learning, or a quantitative research role (or 3+ years with a PhD).
  • Degree in Computer Science, Statistics, Mathematics, Machine Learning, Economics, Physics, or another highly quantitative field.
  • Strong proficiency in Python and SQL, with experience building scalable data and ML pipelines.
  • Deep understanding of statistics, experimentation, causal inference, and predictive modeling.
  • Experience developing and deploying machine learning models in production.
  • Experience working with large-scale datasets and modern data infrastructure.
  • Familiarity with LLMs, embeddings, retrieval systems, RAG, or AI agents.
  • Strong analytical thinking with the ability to translate ambiguous problems into measurable solutions.
  • Excellent communication skills and the ability to work cross-functionally with engineering, product, and leadership.
  • Comfortable operating in a fast-paced, high-ownership startup environment.

Preferred Qualifications

  • Based in NYC with the ability to work from our Brooklyn office, or the ability to work remotely, with travel up to 30% of the year to collaborate in person with the team.
  • Experience building or evaluating LLM-powered products or AI applications.
  • Background in search, recommendation systems, information retrieval, or knowledge graphs.
  • Experience designing A/B tests, offline evaluations, and ML benchmarking frameworks.
  • Familiarity with vector databases, embedding models, and agent orchestration frameworks.
  • Contributions to open-source ML projects or publications in machine learning, NLP, or related fields.
  • Experience in B2B SaaS, developer tools, or enterprise AI products.
  • Based in NYC and able to work from our Brooklyn office, or remote within the U.S. with the ability to travel up to 30% for team collaboration.

Benefits:

  • Compensation & Equity – competitive package aligned with growth, performance, and merit
  • Career Growth Opportunities – be part of a rapidly expanding, global technology company with room to grow professionally

Skills Required

  • 5+ years experience in Data Science, Applied Machine Learning, or quantitative research (or 3+ years with a PhD)
  • Degree in Computer Science, Statistics, Mathematics, Machine Learning, Economics, Physics, or another highly quantitative field
  • Strong proficiency in Python
  • Strong proficiency in SQL
  • Experience building scalable data and ML pipelines
  • Deep understanding of statistics, experimentation, causal inference, and predictive modeling
  • Experience developing and deploying machine learning models in production
  • Experience working with large-scale datasets and modern data infrastructure
  • Familiarity with LLMs, embeddings, retrieval systems, RAG, or AI agents
  • Strong analytical thinking and ability to translate ambiguous problems into measurable solutions
  • Excellent communication skills and ability to work cross-functionally
  • Comfortable operating in a fast-paced, high-ownership startup environment
  • Based in NYC with ability to work from Brooklyn office, or remote with travel up to 30% (preferred)
  • Experience building or evaluating LLM-powered products or AI applications
  • Background in search, recommendation systems, information retrieval, or knowledge graphs
  • Experience designing A/B tests, offline evaluations, and ML benchmarking frameworks
  • Familiarity with vector databases, embedding models, and agent orchestration frameworks
  • Contributions to open-source ML projects or publications in ML/NLP
  • Experience in B2B SaaS, developer tools, or enterprise AI products
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The Company
HQ: New York, NY
25 Employees
Year Founded: 2021

What We Do

Decentralized blockchain applications introduce a myriad of technical challenges, security vulnerabilities and attack vectors. With DeFi (decentralized finance) protocols now securing hundreds of billions in value in production, the stakes have never been higher. With new technological primitives and bustling innovation, new security paradigms are required. dApps leverage open source modules and often consume other protocols in order to create new applications. This introduces a dependency heavy software architecture which makes it an ideal candidate for runtime verification. Chaos Labs is a cloud platform which enables teams to create high-fidelity agent and scenario-based simulations on mainnet forks, offering a real-world testing environment. By providing a rich library of customizable agents and scenarios, teams can rapidly speed up development and reduce their go-to-market time, helping them stay competitive without compromising on the security of their protocol. Additionally, Chaos Labs focuses on developer tooling and cloud infrastructure, allowing teams to move fast on stable infrastructure. The platform is currently live for protocols on the Ethereum network and later expanding to additional blockchains such as Terra, NEAR and Polygon.

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