Lead Data Scientist

Ecolab•Saint Paul, MN

6d•$153,900 - $230,800•Hybrid

About The Position

Ecolab Digital is seeking a commercial solution focused Lead Data Scientist to unlock the value in data assets while creating, maturing, and maintaining unique offerings for customers in the Institutional & Specialty segment. In this role, you will sit at the intersection of AI engineering and data science. You'll be partnering with digital product, marketing, sales, and engineering teams to design, build, and ship intelligent solutions that drive measurable commercial outcomes. You will own the end-to-end execution of AI product development across classical ML, LLM-based applications, and multi-agent/agentic workflows, translating business strategy into production-grade systems that scale.

Requirements

Bachelors Degree in Data Science, Economics, Math, Statistics or related field with an emphasis on analytics or master’s degree with 5 years of experience in progressive data roles.
8 years of experience
5 years of strong Python and SQL. Expert in writing clean, modular, production-grade code, and treating version control, unit testing, and code review as standard practice.
1 year’s shipping production LLM applications: prompt engineering as a real discipline (structured prompts, output schemas, exclusion rules), RAG over vector indexes, and combining GenAI reasoning with deterministic logic for reliable, auditable outputs.
Hands-on experience building and orchestrating multi-agent systems, including sequential handoffs, tool-calling, and scheduled or DAG-based workflows, with good judgment on when to use probabilistic reasoning versus deterministic rules.
Proven experience building and running evaluation and quality frameworks for GenAI output. You can measure whether a prompt or model change actually made things better, calibrate against subject-matter-expert ground truth, and keep hallucinations in check in customer-facing products.
Experience with being a people manager
Proven ability to balance practical business needs with technical rigor, and to clearly explain your approach, assumptions, and tradeoffs to both technical and non-technical audiences.
Excellent communication and presentation skills. You can translate technical work into business context and deliver structured, concise findings to a wide range of stakeholders.
Hands-on experience with PySpark and DataFrame APIs on a large-scale distributed platform (Databricks strongly preferred), and experience governing data pipelines that pull real-world sources (operational, regulatory, sensor/IoT, third-party) into one coherent data model.

Nice To Haves

Deep familiarity with the Databricks GenAI stack: Model Serving, Unity Catalog, Vector Search, MLflow (incl. prompt registry and champion/challenger evaluation), Lakebase/managed PostgreSQL, and Databricks Asset Bundles.
Experience with MCP (Model Context Protocol) servers and tools, conversational AI assistants, and emerging agent-to-agent (A2A) orchestration patterns.
Experience exposing AI capabilities as production services and APIs, and integrating them into customer-facing digital products, with attention to latency, reliability, versioning, and auth.
Proficiency across the Microsoft Azure suite (App Service, Functions, Key Vault, ADO Pipelines) and PowerBI, and comfort with cloud APIs and CI/CD across multiple environments.
Solid data science foundation that carries into GenAI work: EDA, statistical reasoning, metrics and eval design, sampling, and error analysis.
Working knowledge of DevOps, git, Snowflake, and distributed compute platforms.
Experience in Retail/Quick Service Restaurants businesses.
Well-developed and proven leadership, strategic thinking, & business acumen
Sharing a public GitHub profile or project portfolio is encouraged; we'd love to see examples of your hands-on work where available.

Responsibilities

Identify and define opportunities aligned to Institutional & Specialty business challenges, navigating a wide range of problems and solution approaches.
Drive experimentation and delivery of digital data science and AI product innovations aligned to the overall digital product vision.
Partner with a broad range of internal business stakeholders to translate business strategy and VOC into technical opportunities, builds, and lifecycle decisions, communicating clearly and consistently at every level.
Architect and build AI solutions end to end across classical ML, LLM-based applications, and multi-agent systems. You will own the technical design decisions from the data layer through inference, API exposure, and product integration.
Own the production lifecycle of AI agents and models. This covers QA, monitoring, drift detection, prompt and version management, and retraining pipelines, so we ship outcomes that are reliable and credible in customer-facing products.
Define and track effectiveness metrics for AI features in partnership with product and commercial teams.
Design evaluation frameworks with built-in responsible AI practices. This includes champion/challenger pipelines, automated regression testing, guardrails, and audit logging.