Senior Data Scientist - GenAI

Senior Data Scientist / Applied AI Engineer

Hybrid (3 days onsite per week)

120-177K base salary + bonus

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.

• 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 product ionize 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

• 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