Senior Data Scientist, Applied AI and Agentic Solutions Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Scientist, Applied AI and Agentic Solutions Engineer based in the United States. This is a senior, hands-on role focused on applying data science, generative AI, and agentic technologies to complex business challenges.

You will design and develop intelligent solutions that turn large, diverse datasets into actionable insights, automation, and decision-support capabilities.

The role spans the full solution lifecycle, from identifying business needs and experimenting with models to production deployment and optimization.

You’ll work with technologies including Python, LLMs, RAG, vector embeddings, semantic search, cloud platforms, and modern application frameworks.

Success requires combining strong analytical thinking with practical software engineering, responsible AI, security, and governance practices.

You’ll collaborate closely with business, product, analytics, and technology stakeholders to translate strategic opportunities into scalable solutions.

The position offers the opportunity to contribute to emerging AI capabilities while solving meaningful enterprise problems in a collaborative, innovation-focused environment.

Accountabilities

• Model complex business problems and identify meaningful insights using statistical, algorithmic, machine learning, data mining, and visualization techniques.
• Partner with business and product stakeholders to identify analytical questions, define opportunities, and design experiments that address strategic and operational needs.
• Conduct advanced statistical analysis on business and experimental data to validate trends, patterns, hypotheses, and potential solutions.
• Develop predictive models, algorithms, probability engines, and AI-driven capabilities, evaluating their effectiveness against real-world outcomes.
• Design, develop, test, and deploy generative AI and agentic solutions that support enterprise knowledge discovery, automation, and decision-making.
• Build Retrieval-Augmented Generation (RAG) solutions, including document ingestion, chunking, embeddings, vector storage, retrieval strategies, prompt engineering, and response generation.
• Develop software, algorithms, and automated processes to cleanse, integrate, transform, and analyze large datasets from disparate sources.
• Apply large language models, machine learning, deep learning, semantic search, vector embeddings, and emerging AI technologies to create new data-driven capabilities.
• Translate business requirements into scalable, secure, maintainable technical solutions across the full lifecycle, from discovery and design through implementation, testing, adoption, and ongoing improvement.
• Evaluate AI-generated outputs for accuracy, reliability, security, performance, and business value, continuously optimizing models and solutions based on results.
• Build and maintain production-ready solutions with appropriate monitoring, observability, reliability, scalability, and operational support.
• Collaborate with analytics, product, user experience, information architecture, and engineering teams to improve dashboards, visualization, experimentation, and data-driven experiences.
• Communicate complex analytical findings, technical concepts, and recommendations clearly to business leaders, product teams, and other stakeholders.
• Support enterprise data governance and promote responsible practices around data privacy, security, AI governance, and compliance.
• Research emerging data science, AI, and software engineering techniques and apply relevant innovations to business challenges.

Requirements

• Bachelor’s degree in computer science, data science, statistics, economics, or a related discipline, or equivalent professional experience.
• At least 6 years of experience in analytics, data science, machine learning, AI solution development, data and AI engineering, cloud platform engineering, or a related field.
• Demonstrated experience developing and deploying AI, generative AI, and analytics solutions for enterprise knowledge discovery, automation, or decision support.
• Experience working across the complete solution lifecycle, including requirements gathering, design, development, testing, implementation, deployment, and user adoption.
• Strong proficiency in Python and familiarity with modern application tec…