New opportunity

Sr Data Scientist

Ecolab Inc. · Bangalore, India

About this role

Responsibilities

Partner with product and business stakeholders to translate ambiguous asks into clear AI/ML use cases. Own product relationships for assigned use cases by managing expectations, surfacing risks early, and aligning stakeholders around tradeoffs and business outcomes. Lead exploratory analysis, feature engineering, model selection, experiment design, and statistical validation for time series, forecasting, anomaly detection, and other IoT-oriented use cases. Work with IoT and sensor-based data, including irregular intervals, missingness, and event-driven signals. Define strong baseline approaches and recommend the simplest effective solution. Build and evaluate predictive, optimization, and GenAI-enabled solutions using reproducible workflows in Databricks. Help define and deliver data products that are reusable, maintainable, and valuable to downstream users, systems, or business processes. Use GitHub and Azure DevOps with strong version control, pull request discipline, documentation, and work tracking practices. Contribute to API-oriented solution design by shaping model inputs/outputs, integration expectations, and consumption patterns for downstream applications. Mentor junior and mid-level data scientists and contribute reusable templates and team standards. Required Qualifications: 6+ years of experience in data science, machine learning, or applied AI with a track record of delivering business-impacting solutions. Strong programming skills in Python, PySpark, and SQL. Solid grounding in statistics, machine learning, experimentation, and model evaluation. Hands-on experience with Databricks for exploratory analysis, model development, and reproducible ML workflows. Demonstrated experience with time series modeling, forecasting, anomaly detection, and/or sequential data problems. Experience working with IoT, sensor, telemetry, or other operational data sources. Strong data engineering capability, including data wrangling at scale, feature pipeline design, dataset preparation, data quality troubleshooting, and support for production-ready analytical workflows. Strong ability to work across GitHub and Azure DevOps workflows. Demonstrated ability to communicate clearly with technical and non-technical stakeholders. Preferred Skills: API design and integration, model monitoring, mentoring experience, familiarity with cloud-native deployment patterns, and familiarity with MLflow.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonPySparkSQLStatisticsMachine LearningDatabricksMLflowTime Series ModelingForecastingAnomaly DetectionIoTData EngineeringFeature EngineeringGitHubAzure DevOpsAPI Design

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Translating Ambiguous Business Needs into AI/ML Use Cases
The role requires bridging the gap between non-technical stakeholders and technical execution. You must demonstrate the ability to frame vague business problems into structured, actionable machine learning projects while managing stakeholder expectations regarding outcomes and risks.
Developing Robust IoT and Time-Series Solutions
A core technical priority is handling complex, sensor-based data characterized by irregular intervals, missing values, and event-driven signals. You must be proficient in feature engineering and model selection specifically for forecasting and anomaly detection in operational environments.
Building Scalable and Maintainable Data Products
Beyond model development, you are expected to deliver reusable, production-ready data products. This involves using Databricks for reproducible workflows, adhering to strict version control via GitHub/Azure DevOps, and designing API-oriented solutions for seamless downstream integration.
Technical Leadership and Mentorship
As a senior member, you are responsible for defining team standards, creating reusable templates, and mentoring junior to mid-level data scientists to elevate the overall technical quality and consistency of the team's output.

Likely challenges to think through

Managing Stakeholder Expectations in Ambiguous Projects
The challenge lies in balancing business pressure for results with the inherent uncertainty of AI/ML research. Prepare by practicing how you communicate technical risks, define 'simplest effective' baselines, and pivot strategies when initial exploratory analysis reveals data limitations.
Data Quality and Complexity in IoT Environments
Working with real-world sensor data often involves significant noise and data gaps. Prepare by highlighting your experience in data wrangling at scale and your systematic approach to troubleshooting data quality issues before they impact model performance.
Integrating Models into Production Systems
Moving from a notebook-based experiment to an API-integrated production service is a common hurdle. Prepare by focusing on your experience with API design, cloud-native deployment patterns, and the discipline required for maintaining production-ready analytical workflows.

Practice your answers

Technical practice

Handling Irregular IoT Time Series Data

What to coverExplain your approach to preprocessing sensor data with irregular intervals and missing values. Discuss specific techniques for resampling, interpolation, or feature engineering that maintain signal integrity for downstream forecasting models.

Designing Robust Anomaly Detection Systems

What to coverDescribe how you would build an anomaly detection pipeline for operational telemetry. Focus on how you define baselines, select appropriate algorithms, and validate performance when ground truth labels are scarce or imbalanced.

Databricks and Reproducible ML Workflows

What to coverDiscuss how you leverage Databricks and MLflow to ensure reproducibility in your model development. Suggestion: Focus on versioning datasets, tracking experiments, and managing environment dependencies to ensure production-ready code.

API-Oriented Solution Design

What to coverWhen designing an AI solution for downstream consumption, how do you define input/output contracts? Address how you balance model complexity with the integration requirements of external applications or business systems.

Feature Engineering for Sequential Data

What to coverWhat strategies do you use to extract meaningful features from event-driven signals? Discuss the trade-offs between manual feature engineering and automated approaches in the context of IoT-oriented use cases.

Evaluating Predictive Models in Production

What to coverBeyond standard metrics like RMSE or F1-score, how do you evaluate the business impact of a model? Suggestion: Discuss how you design evaluation frameworks that account for model drift and the specific needs of operational stakeholders.

Data Engineering at Scale with PySpark

What to coverDescribe a scenario where you had to optimize a data pipeline for large-scale processing. Focus on your use of PySpark to handle data wrangling, quality checks, and the creation of reusable feature pipelines.

Version Control and CI/CD for Data Science

What to coverHow do you integrate GitHub and Azure DevOps into your daily development cycle? Explain your process for managing pull requests, documentation, and tracking work to ensure team standards are met.

Behavioural practice

Translating Ambiguous Business Requirements

What to coverDescribe a time you had to turn an ambiguous request from a stakeholder into a concrete AI/ML project. Use the STAR method to explain how you clarified the scope, aligned on business outcomes, and managed expectations.

Managing Stakeholder Trade-offs

What to coverGive an example of a project where you had to negotiate trade-offs between model performance and business constraints. How did you communicate these risks and reach a consensus with non-technical stakeholders?

Mentoring and Setting Standards

What to coverProvide an example of how you have contributed to team standards or mentored junior data scientists. Focus on how you helped improve the team's technical practices or code quality through reusable templates or code reviews.

Influencing Decisions with Evidence

What to coverDescribe a situation where you used data to influence a product or business decision. Explain how you presented your findings to ensure the stakeholders understood the evidence and felt confident in the recommended path forward.

Sharpen your knowledge

Topics to prepare

Time Series and IoT Data Modeling
The role requires handling irregular intervals, missingness, and event-driven signals in sensor data. Review techniques for resampling, interpolation, and feature engineering specifically for sequential telemetry data to ensure robust forecasting and anomaly detection.
Stakeholder Management and Problem Framing
The position involves translating ambiguous business requests into clear AI/ML use cases. Practice articulating technical tradeoffs, managing expectations, and aligning stakeholders on business outcomes to demonstrate your ability to own product relationships.
Production-Ready ML Workflows
Focus on designing reusable, maintainable data products. Review concepts for building reproducible pipelines, including model evaluation frameworks, error analysis, and the integration of API-oriented solution designs for downstream consumption.

Skills to revise

Databricks and MLflow
Gain hands-on proficiency in Databricks for exploratory analysis and reproducible workflows. Practice using MLflow for tracking experiments and managing the model lifecycle, as this is a core requirement for the role.
Version Control and CI/CD Workflows
Demonstrate strong practices in GitHub and Azure DevOps. Practice creating pull requests, managing version control, and documenting code to align with the team's standards for production-ready analytical workflows.
PySpark and Data Engineering at Scale
Refine your ability to perform data wrangling and feature pipeline design at scale. Practice troubleshooting data quality issues and preparing datasets specifically for IoT-oriented analytical workflows.

Your preparation checklist

Prepare a case study on IoT time-series modeling
Develop a concise walkthrough of a past project involving sensor or telemetry data. Focus on how you handled irregular intervals, missing data, and event-driven signals, as these are specific requirements for this role.
Demonstrate Databricks and MLflow proficiency
Prepare to discuss your experience building reproducible ML workflows within Databricks. Highlight how you have used MLflow for experiment tracking, model versioning, and managing the lifecycle of predictive or GenAI models.
Refine your approach to stakeholder communication
Prepare examples of how you have translated ambiguous business problems into technical AI/ML use cases. Be ready to explain how you manage stakeholder expectations, surface risks early, and align teams on trade-offs.
Review your data engineering and API design experience
Prepare to discuss your ability to build production-ready data pipelines and your experience in shaping model inputs/outputs for API-oriented integrations. Emphasize your familiarity with version control practices using GitHub and Azure DevOps.
Formulate questions for the interviewers
Prepare thoughtful questions regarding the team's current data product roadmap, how they prioritize IoT use cases, the existing standards for model monitoring, and how the team balances technical debt with the need for rapid delivery.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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