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About This Role
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.
Position Details
- St. Paul, MN or Naperville, IL with a hybrid schedule.
- Up to 10% of the time.
What you will do
- 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.
Minimum Qualifications
- 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.
- Immigration sponsorship is not available for this position.
Preferred Qualifications
- 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.
Annual or Hourly Compensation Range
The base salary range for this position is $153,900.00 - $230,800.00. This position is eligible for annual bonus pay based on performance, per plan terms. Many factors are taken into consideration when determining compensation, such as experience, education, training, geography, etc. We comply with all minimum wage and overtime laws. Benefits
Ecolab strives to provide comprehensive and market-competitive benefits to meet the needs of our associates and their families.
If you are viewing this posting on a site other than our Ecolab Career website, view our benefits at jobs.ecolab.com/working-here.
Potential Customer Requirements Notice
To meet customer requirements and comply with local or state regulations, applicants for certain customer-facing roles may need to:
- Undergo additional background screens and/or drug/alcohol testing for customer credentialing.
Americans with Disabilities Act (ADA)
Ecolab will provide reasonable accommodation (such as a qualified sign language interpreter or other personal assistance) with our application process upon request as required to comply with applicable laws. If you have a disability and require accommodation assistance in this application process, please visit the Recruiting Support link in the footer of each page of our career website.
Our Commitment to a Culture of Inclusion & Belonging
At Ecolab, we believe the best teams are inclusive. We are on a journey to create a workplace where every associate can grow and achieve their best. We are committed to fair and equal treatment of associates and applicants and recruit, hire, promote, transfer and provide opportunities for advancement based on individual qualifications and job performance. In all matters affecting employment, compensation, benefits, working conditions, and opportunities for advancement, we will not discriminate against any associate or applicant for employment because of race, religion, color, creed, national origin, citizenship status, sex, sexual orientation, gender identity and expressions, genetic information, marital status, age, disability, or status as a covered veteran.
In addition, we are committed to furthering the principles of Equal Employment Opportunity (EEO) through Affirmative Action (AA).
We will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, and the New York City Fair Chance Act.
Salary Context
This $153K-$230K range is above the median for Data Scientist roles in our dataset (median: $167K across 172 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Ecolab, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $153K to $230K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Ecolab AI Hiring
Ecolab has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Naperville, IL, US, Bengaluru East, Karnataka, India. Compensation range: $230K - $230K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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