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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. Responsibilities include identifying business opportunities for AI/ML, developing predictive and prescriptive models, creating reusable analytical assets, and maintaining data pipelines. The role also involves deploying and monitoring models using MLOps practices, contributing to enterprise data governance, and facilitating requirements gathering. Required qualifications include a Bachelor's degree in a relevant field and 5-10 years of experience in Data Science, Analytics, Data Engineering, or Business Intelligence. Candidates should have experience in manufacturing or global enterprise environments and working with cross-functional international teams. Technical proficiency is required in Python, R, SQL, Machine Learning, and various Azure and Databricks platforms. A Master's degree is considered an asset.

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 LearningDatabricksDelta LakeUnity CatalogMLflowPySparkSpark SQLPower BIDAXData Vault 2.0Kimball Dimensional ModelingData GovernanceGitAzure DevOpsCI/CD PipelinesAgile

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 Model Lifecycle Management
The role requires not just developing predictive and prescriptive models, but actively managing them through MLOps practices. You must demonstrate proficiency in deploying, monitoring, and maintaining model performance using tools like MLflow, Azure Machine Learning, and CI/CD pipelines to ensure reproducibility and governance.
Cross-Functional Stakeholder Engagement
You will act as a consultant between business units and technical teams. A core priority is facilitating requirements gathering and translating complex analytical findings into actionable business recommendations for both technical and non-technical audiences to drive adoption.
Enterprise Data Integration and Quality
You are expected to contribute to the broader Enterprise Data Management strategy. This involves profiling and preparing data from diverse sources like ERP, CRM, and manufacturing systems, while adhering to Data Vault 2.0 and Kimball dimensional modeling standards to ensure high data quality.
Scalable Analytical Solution Development
The focus is on creating reusable analytical assets and accelerators within the Azure and Databricks ecosystem. You must be capable of building scalable data pipelines and reporting solutions that directly improve operational efficiency and strategic decision-making.

Likely challenges to think through

Navigating Complex Manufacturing Data Landscapes
Working in a global manufacturing environment often involves siloed or unstructured data from IoT and legacy systems. Prepare by highlighting your experience in harmonizing disparate data sources and your ability to apply analytical rigor to industrial operational challenges.
Balancing Technical Depth with Business Value
The role demands high technical proficiency in Databricks and Azure, yet success is measured by business impact and user adoption. The challenge is to avoid over-engineering solutions; prepare to discuss how you prioritize projects based on measurable business benefits and stakeholder needs.
Maintaining Governance in a Rapidly Evolving Tech Stack
With the integration of Microsoft Fabric, Databricks, and various cloud platforms, maintaining data consistency and governance is difficult. Prepare by demonstrating your experience with metadata management and data quality frameworks within a large-scale, multi-platform enterprise environment.

Practice your answers

Technical practice

Implementing MLOps with Databricks and MLflow

What to coverDescribe your experience using MLflow for model versioning and tracking within a Databricks environment. Explain how you ensure model reproducibility and manage the lifecycle from development to production deployment.

Data Modeling: Data Vault 2.0 vs. Kimball

What to coverCompare the use cases for Data Vault 2.0 and Kimball dimensional modeling in an enterprise environment. Discuss how you decide which methodology to apply when designing analytical data models for complex manufacturing or ERP data.

Optimizing Data Pipelines in Azure

What to coverExplain your approach to building and maintaining scalable data pipelines using Azure Data Factory and Databricks. Focus on how you handle data quality, error handling, and performance optimization for large-scale datasets.

Predictive Modeling in Manufacturing

What to coverDiscuss a time you developed a predictive or prescriptive model to solve a manufacturing or industrial business problem. Detail your process for feature engineering and how you validated the model's performance before deployment.

Leveraging Microsoft Fabric and Azure Synapse

What to coverHow have you utilized Microsoft Fabric or Azure Synapse Analytics to unify data management and reporting? Explain the trade-offs you consider when choosing between these platforms for enterprise-wide analytical solutions.

Ensuring Data Governance and Quality

What to coverDescribe your experience contributing to enterprise data governance, specifically regarding metadata management and data quality initiatives. Suggestion: Prepare to discuss how you balance strict governance standards with the need for agile data delivery.

Advanced Power BI and DAX

What to coverExplain how you design complex semantic models in Power BI to support executive reporting. Provide an example of a challenging DAX calculation you implemented to derive actionable business insights.

CI/CD for Data Science Projects

What to coverDetail your experience integrating Git and Azure DevOps into your data science workflow. Explain how you automate testing and deployment for machine learning models to ensure consistent production performance.

Behavioural practice

Bridging Technical and Business Teams

What to coverDescribe a situation where you acted as a consultant between technical delivery teams and non-technical business stakeholders. How did you facilitate requirements gathering to ensure the final analytical solution met their specific business needs?

Managing Cross-Functional International Collaboration

What to coverProvide an example of working with a cross-functional, international team on a complex data project. How did you handle communication challenges or differing priorities to ensure the project was delivered on time?

Driving User Adoption of Analytical Tools

What to coverTell me about a time you delivered a dashboard or analytical tool that initially faced low user adoption. What steps did you take to understand the barriers and improve the tool's usability and value for the end users?

Prioritizing Business Value in Analytics

What to coverDescribe a scenario where you had to identify a new business opportunity for AI/ML. How did you evaluate the potential impact and justify the project's scope to stakeholders?

Sharpen your knowledge

Topics to prepare

MLOps and Model Lifecycle Management
The role requires deploying, monitoring, and governing models in production. Review the end-to-end MLOps lifecycle, specifically focusing on model versioning, performance monitoring, and reproducibility using MLflow and Azure Machine Learning to ensure models meet enterprise standards.
Enterprise Data Architecture Methodologies
Husky utilizes Data Vault 2.0 and Kimball Dimensional Modeling for their enterprise data strategy. Study these modeling techniques to understand how to design scalable analytical data models that integrate diverse sources like ERP, CRM, and IoT data.

Skills to revise

Databricks Lakehouse and PySpark
Practice building data pipelines using PySpark and Spark SQL within the Databricks environment. Focus on Delta Lake operations and Unity Catalog for data governance, as these are central to the company's cloud-based data platform.
Azure Data Integration and CI/CD
Develop proficiency in orchestrating data workflows using Azure Data Factory and implementing CI/CD pipelines via Azure DevOps. This is essential for the role's requirement to maintain automated, production-ready data and model pipelines.
Power BI and DAX for Enterprise Reporting
Refine your ability to create complex semantic models and executive dashboards using Power BI. Focus on writing efficient DAX measures to translate technical analytical findings into actionable business insights for stakeholders.

Your preparation checklist

Map your experience to manufacturing or global enterprise use cases
Prepare to discuss specific examples where you applied predictive modeling or machine learning to solve operational efficiency, product quality, or supply chain challenges in a large-scale, cross-functional environment.
Demonstrate proficiency in the Azure and Databricks ecosystem
Create a summary of your experience building end-to-end pipelines using Azure Data Factory, Databricks, and Delta Lake, specifically highlighting how you have utilized Unity Catalog for governance and MLflow for model lifecycle management.
Prepare a technical walkthrough of an MLOps implementation
Develop a case study detailing a model you deployed into production, focusing on how you utilized CI/CD pipelines in Azure DevOps, implemented model monitoring, and ensured reproducibility.
Review Data Vault 2.0 and Kimball modeling methodologies
Be ready to explain your experience with enterprise data architecture, specifically how you have applied Data Vault 2.0 or Kimball dimensional modeling to structure data for analytical consumption.
Formulate questions regarding Husky's data strategy
Prepare thoughtful questions about how the team balances immediate business reporting needs with long-term Enterprise Data Management goals and how they currently integrate IoT data into their analytics platform.

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