Data Scientist

Overview

Work as a Data Scientist on Ecolab Digital's Institutional & Specialty team, turning operational data into prioritized, plain-language insights and recommendations for customers and internal teams. Take data science and GenAI problems from exploration and modeling through deployment, monitoring, and customer-facing decision support.

What you'll do

• Build, test, and deploy analytical and machine learning solutions including classification, forecasting, summarization, Q&A, recommendations, and decision support using diverse internal and external data sources.
• Train, tune, and evaluate machine learning models, and defend choices with error analysis.
• Build GenAI applications on LLM APIs using structured prompts with output schemas, retrieval-augmented generation over vector indexes, and GenAI reasoning combined with deterministic logic.
• Contribute to multi-step AI workflows for task- and goals-based agents.
• Prepare, structure, and validate datasets.
• Deploy solutions into cloud environments using established engineering and CI/CD practices.
• Monitor deployed models and AI systems, supporting performance, reliability, latency, and cost efficiency after launch.
• Build dashboards and visualizations that turn model output into actionable decisions.
• Translate product, engineering, and business requirements into scalable solutions.
• Communicate insights and impact clearly to technical and non-technical audiences.

What you'll need

• Bachelor's degree in Data Science, Computer Science, Math, Statistics, or a related field with 3 years of hands-on data science experience, or a Master's degree in a related field with 1 or more years of hands-on experience.
• 3+ years of Python.
• Clean, modular coding practices and standard use of version control, testing, and code review.
• 3+ years of SQL for querying and preparing data.
• Hands-on experience with PySpark and DataFrame APIs on a large-scale distributed platform.
• 2+ years building machine learning models, covering training, tuning, and evaluation.
• Classical machine learning and statistics, including anomaly detection and composite scoring.
• 1+ year building GenAI or LLM solutions, including RAG pipelines, agents, or applications; personal projects included.
• Ability to show what you built and the problem it solved.
• Experience taking models and solutions all the way to production, including fixing, improving, and re-releasing them after launch.
• Ability to explain what you put into production, who used it, and what you changed after launch.
• Ability to evaluate whether changes to a prompt, model, or pipeline genuinely improved output by comparing results against a trusted answer key, monitoring quality after launch, and controlling errors and hallucinations.
• Working knowledge of Git, agile practices, and CI/CD workflows.
• Experience building dashboards and data visualizations to communicate results; Power BI preferred.
• Strong analytical thinking, problem-solving, and ability to explain approaches, assumptions, and trade-offs to a range of stakeholders.
• Solid data science foundation covering EDA, statistical reasoning, metric and evaluation design, sampling, and error analysis.

Nice to have

• Experience on the Databricks platform, including Spark SQL, Delta Lake, Unity Catalog, MLflow, Vector Search, and Model Serving.
• Hands-on experience with GenAI frameworks and tools including LangChain, Anthropic, OpenAI or Hugging Face APIs, and vector databases.
• Exposure to agentic or multi-step AI workflows such as tool-calling and sequential handoffs.
• Proficiency across the Microsoft Azure suite and comfort with cloud APIs across multiple environments.
• Experience in Retail or Quick Service Restaurant businesses.
• Sharing a public GitHub profile or project portfolio is encouraged.

Details

• Location: Bangalore, Karnataka, India.