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Data Scientist- Husky( India ) Guindy,Chennai

Husky Technologies · Chennai, India

About this role

The Data Scientist is a key member of Husky's global Enterprise Data & Analytics organization, responsible for transforming enterprise data into actionable business insights, predictive intelligence, and AI-driven solutions. This role partners with business stakeholders, data engineers, architects, and analysts to design, develop, and deploy advanced analytics, machine learning models, and enterprise reporting solutions. The role leverages modern cloud-based data platforms including Azure, Databricks, Microsoft Fabric, Power BI, and Data Vault methodologies to support Husky's Enterprise Data Management strategy. Key responsibilities include identifying business opportunities for AI/ML, designing and deploying predictive/prescriptive models, developing data pipelines, creating analytical dashboards, and implementing MLOps practices. Candidates must have a Bachelor's degree in a relevant field (Master's preferred) and 5-10 years of experience in Data Science, Analytics, Data Engineering, or Business Intelligence, ideally within manufacturing or global enterprise environments. Proficiency in Python, R, SQL, Machine Learning, and cloud platforms like Azure and Databricks is required. The role involves cross-functional collaboration, project engagement, and adherence to data governance and quality standards.

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

Skills for this role

PythonRSQLMachine LearningStatistical AnalysisPredictive ModelingFeature EngineeringGenerative AIMicrosoft AzureAzure Data Lake StorageAzure Data FactoryAzure Synapse AnalyticsMicrosoft FabricAzure Machine LearningDatabricks Lakehouse PlatformDatabricks WorkflowsDelta LakeUnity CatalogMLflowPySparkSpark SQLStructured StreamingDatabricks Asset BundlesPower BIDAXData Vault 2.0Kimball Dimensional ModelingGitAzure DevOpsCI/CD Pipelines

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

End-to-End MLOps Implementation
The role requires not just building models but ensuring they are production-ready, reproducible, and governed. You must demonstrate proficiency in the full lifecycle using Databricks MLflow, Azure Machine Learning, and CI/CD pipelines to ensure models are monitored and performant in a global enterprise environment.
Enterprise Data Integration and Quality
You will be responsible for transforming complex data from ERP, CRM, and manufacturing systems. Success depends on your ability to apply Data Vault 2.0 and Kimball methodologies to ensure data consistency, quality, and compliance across the organization's data landscape.
Business-Aligned Analytical Solutions
You must translate technical analytical findings into actionable business insights. This requires partnering with stakeholders to define KPIs and building intuitive Power BI dashboards that drive measurable improvements in operational efficiency and decision-making.

Likely challenges to think through

Navigating Complex Manufacturing Data Ecosystems
Integrating data from diverse sources like IoT, manufacturing, and global ERP systems presents significant data cleaning and architectural challenges. Prepare by highlighting your experience in handling heterogeneous datasets and your ability to maintain data integrity within a large-scale enterprise framework.
Balancing Technical Depth with Stakeholder Communication
The role acts as a consultant between technical delivery teams and business stakeholders. You may face difficulty explaining complex AI/ML model outputs to non-technical users. Prepare by practicing how to simplify technical concepts into business-focused recommendations without losing accuracy.
Adherence to Strict Governance and Standards
Operating within a global enterprise requires strict compliance with data governance, metadata management, and security standards. You will need to demonstrate how you balance the need for rapid innovation in AI/ML with the necessity of maintaining rigorous, reproducible, and compliant data practices.

Practice your answers

Technical practice

Implementing MLOps with Databricks and MLflow

What to coverExplain how you would use MLflow to manage the lifecycle of a machine learning model from experimentation to production. Focus on model versioning, tracking experiments, and ensuring reproducibility within a Databricks environment.

Data Vault 2.0 vs. Kimball Dimensional Modeling

What to coverCompare the use cases for Data Vault 2.0 and Kimball modeling in an enterprise environment. Discuss when you would choose one over the other for structuring data pipelines and analytical models.

Optimizing Data Pipelines in Azure

What to coverDescribe your approach to building scalable data pipelines using Azure Data Factory and Databricks. Address how you handle data quality, error logging, and performance optimization for large-scale manufacturing datasets.

Feature Engineering for Predictive Maintenance

What to coverGiven a manufacturing scenario involving IoT sensor data, describe your process for feature engineering. Explain how you would handle time-series data and identify relevant signals for predictive maintenance models.

Power BI Semantic Modeling and DAX

What to coverDiscuss the importance of well-structured semantic models in Power BI. Explain how you use DAX to create complex metrics and ensure performance when reporting on large enterprise datasets.

Ensuring Data Governance and Quality

What to coverHow do you integrate data governance and quality checks into your development workflow? Suggest strategies for maintaining metadata management and data consistency across disparate sources like ERP and CRM systems.

CI/CD for Data Science Projects

What to coverExplain how you implement CI/CD pipelines for machine learning models using Git and Azure DevOps. Focus on the automation of testing, deployment, and monitoring to ensure production stability.

Handling Structured Streaming in Databricks

What to coverDescribe a scenario where you would use Structured Streaming in Databricks. Explain the trade-offs between batch and streaming processing for real-time analytical insights.

Behavioural practice

Bridging the Gap Between Technical and Non-Technical Stakeholders

What to coverDescribe a time you had to explain a complex machine learning model or analytical finding to a non-technical business stakeholder. How did you ensure they understood the business value and the limitations of the model?

Managing Conflicting Requirements in Analytics Projects

What to coverTell me about a situation where you had to balance the requirements of different business units while designing an analytical solution. How did you prioritize features and ensure the final output met the needs of all stakeholders?

Driving Adoption of Analytical Tools

What to coverProvide an example of a project where you were responsible for user adoption of a new dashboard or analytical tool. What steps did you take to ensure the business actually used the solution to improve their decision-making?

Handling Project Setbacks or Data Quality Issues

What to coverDescribe a time when you encountered a significant data quality issue or project delay during a deployment. How did you identify the root cause, communicate the impact to stakeholders, and resolve the issue?

Sharpen your knowledge

Topics to prepare

Data Vault 2.0 and Kimball Modeling
Review the core principles of Data Vault 2.0 and Kimball dimensional modeling. Practice mapping complex manufacturing or ERP data schemas into these architectures to ensure you can support Husky's enterprise data management strategy.
MLOps Lifecycle and Governance
Study the end-to-end MLOps lifecycle, focusing on model versioning, monitoring, and reproducibility. Prepare to discuss how to maintain model performance and explainability in production environments using tools like MLflow.
Enterprise Data Governance
Understand the fundamentals of data quality management, metadata management, and master data management (MDM). Focus on how these practices ensure data consistency and compliance within a global enterprise environment.

Skills to revise

Databricks Lakehouse and PySpark
Practice building data pipelines using PySpark and Spark SQL within the Databricks environment. Focus on utilizing Delta Lake for reliable data storage and Unity Catalog for managing data access and governance.
Azure Cloud Analytics Stack
Gain hands-on experience with Azure Data Factory for orchestration and Microsoft Fabric for integrated analytics. Ensure you can integrate these services to support end-to-end data preparation and reporting workflows.
Power BI and DAX
Develop advanced Power BI dashboards that translate complex analytical findings into actionable business insights. Practice writing complex DAX measures to support executive reporting and self-service analytics requirements.

Your preparation checklist

Map your experience to manufacturing use cases
Prepare to discuss how you have applied predictive modeling or anomaly detection to industrial, manufacturing, or supply chain datasets. Focus on how your models directly improved operational efficiency or product quality, as these are key success metrics for Husky.
Demonstrate Databricks and Azure proficiency
Create a brief technical summary or project walkthrough highlighting your experience with the Databricks Lakehouse platform, specifically using PySpark, Delta Lake, and MLflow for model lifecycle management. Be ready to explain how you integrate these with Azure Data Factory and CI/CD pipelines.
Prepare a Data Vault and Kimball case study
Review your past work involving enterprise data architecture. Be prepared to explain your approach to data modeling using Data Vault 2.0 or Kimball methodologies and how you have ensured data quality and governance in large-scale enterprise environments.
Develop a Power BI dashboard narrative
Select a project where you translated complex analytical findings into a Power BI dashboard. Prepare to explain the business problem, the DAX logic used, and how the resulting insights influenced stakeholder decision-making.
Formulate questions for the hiring team
Prepare thoughtful questions regarding Husky's current Enterprise Data Management roadmap, the specific manufacturing domains they are prioritizing for AI/ML, and how the data science team collaborates with global cross-functional stakeholders.

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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