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Senior Data Scientist

United States, New York 120000.00 - 177000.00 USD / Year · Job Posted February 21, 2026
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Job Description

We’re partnering with a major enterprise organization undergoing significant investment in AI and data capabilities. This role sits within a central AI function focused on building production-grade machine learning and generative AI solutions that improve customer experience, operational efficiency, and decision intelligence across the business. You’ll work on real, deployed AI systems - collaborating closely with product, engineering, and business stakeholders to design, build, and scale intelligent applications.

Job Responsibility

  • Deliver AI and machine learning solutions that solve real operational and customer-facing challenges
  • Contribute across the full model lifecycle — from data exploration and feature engineering through to deployment, monitoring, and iteration
  • Build and productionize ML and GenAI solutions using modern cloud and data platforms
  • Design and evaluate intelligent automation solutions using LLMs, retrieval systems, and agent-style architectures
  • Implement and optimize RAG pipelines, including embeddings, vector search, retrieval tuning, and prompt orchestration
  • Expose AI capabilities through APIs, internal tools, and workflow applications used by business teams
  • Build rapid prototypes and lightweight interfaces to support validation and adoption
  • Follow best practices around model governance, testing, monitoring, and CI/CD in collaboration with platform and MLOps teams

Requirements

  • Advanced degree in Computer Science, Engineering, Mathematics, Statistics, or similar quantitative field
  • 7+ years applying data science, machine learning, or applied AI in production environments
  • Strong Python and SQL skills
  • Solid understanding of software engineering fundamentals (version control, testing, logging, deployment workflows)
  • Experience working with modern cloud and data platforms (e.g. AWS-based ML tooling, enterprise data warehouses, distributed compute platforms)
  • Practical exposure to LLMs, RAG architectures, or agent-based systems
  • Strong grounding in core ML concepts including feature engineering, model evaluation, and classical ML approaches (e.g. tree-based models, supervised/unsupervised learning)
  • Ability to communicate technical work clearly to non-technical stakeholders and influence decision making

What we offer

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