Staff Applied Data Scientist, Agentic AI

About Emergence AI

Emergence AI is building AI agents that don't just generate answers—they investigate, reason, and help organizations make better decisions. Our platform enables enterprises to deploy intelligent agents that understand complex data, collaborate across business systems, and automate high-value work with the transparency, traceability, and reliability required for production environments. Founded by veterans of IBM Research, Google, Microsoft, and Amazon, we're a small team of researchers, engineers, and builders working at the intersection of frontier AI research and real-world customer challenges. Everyone owns meaningful work, collaborates directly with customers, and helps shape the future of our platform.

About the Role

We're looking for a Staff Applied Data Scientist to help define how AI agents investigate enterprise data, explain their reasoning, and support better business decisions. You'll combine statistical modeling, machine learning, and LLM-powered workflows to solve complex customer problems from end to end, working alongside AI engineers, researchers, product teams, and customers to transform messy enterprise data into intelligent systems that uncover insights and recommend meaningful actions.

This is a highly hands-on Staff-level role with significant technical ownership. You'll own customer problems from discovery through production deployment, helping build reusable capabilities that become core components of Emergence AI's platform.

Responsibilities

• Build the statistical and machine learning capabilities that power autonomous AI agents operating on complex enterprise data.
• Design models for anomaly detection, forecasting, optimization, root-cause analysis, and decision support.
• Analyze large, multi-source enterprise datasets spanning structured, unstructured, operational, and time-series data.
• Partner with AI engineers to build agentic workflows that can query data, evaluate evidence, generate insights, and support customer decisions.
• Translate ambiguous customer challenges into scalable, production-ready AI solutions.
• Develop reusable analytical capabilities that become core platform features used across multiple customer deployments.
• Establish best practices for experimentation, model evaluation, governance, and operational reliability.
• Mentor engineers and data scientists while helping raise the technical bar across the organization.

Requirements

• Significant experience building production machine learning or applied data science systems.
• Strong Python skills and experience with modern machine learning and data science libraries.
• Deep expertise in statistical modeling, machine learning, experimentation, and model evaluation.
• Experience working with large, complex enterprise or operational datasets.
• A track record of taking ambiguous technical problems from discovery through production deployment.
• Excellent communication skills with the ability to explain complex technical concepts to engineers, customers, and executive stakeholders.
• A collaborative mindset and passion for mentoring others while continuing to learn and grow.

Bonus Points

• Experience building AI agents, LLM-powered applications, or RAG and tool-calling workflows.
• Experience with agent frameworks such as LangGraph, CrewAI, AutoGen, ADK, or similar technologies.
• Experience with model evaluation, observability, or human feedback systems.
• Experience with modern cloud and data platforms such as AWS, GCP, Azure, Databricks, Snowflake, Spark, or Kubernetes.