Advanced Analytics Engineer
Posted
Summary
Senior analytics engineer (7-10 yrs) in Chennai working from office on rotational UK/US shifts, building advanced analytics, ML, and generative AI solutions for business stakeholders. Core stack: Microsoft Fabric, Power BI, Python/R, SQL, PySpark, and Git-based CI/CD.
Position – Advanced Analytics Engineer
Location : Chennai (WFO)
Experience : 7-10 Years
Shift : 2.00 PM to 11.00 PM (UK shift) and 5:30 PM to 2:30 AM (US Shift) – Rotational Basis
Key Responsibilities
• Partner with business stakeholders to understand business challenges, identify analytical opportunities, and translate requirements into scalable solutions.
• Design and develop advanced analytics models for forecasting, optimization, segmentation, classification, and predictive decision-making.
• Perform exploratory data analysis to identify patterns, trends, drivers, anomalies, and actionable business insights.
• Build, validate, deploy, and monitor machine learning and statistical models for enterprise business use cases.
• Develop analytical solutions using Python, R, SQL, Microsoft Fabric, notebooks, and related technologies.
• Create dashboards, scorecards, and self-service analytical experiences using Microsoft Power BI and Fabric.
• Collaborate with data engineers to define data requirements and ensure reliable, governed, analytics-ready datasets.
• Support AI and Generative AI initiatives by assessing business use cases and implementing appropriate analytical approaches.
• Build reusable analytics assets, feature engineering components, notebooks, templates, and frameworks.
• Document analytical methodologies, assumptions, model logic, validation results, limitations, and business recommendations.
• Present analytical insights and recommendations clearly to business and executive stakeholders.
• Apply data governance, privacy, security, and responsible AI standards throughout the analytics lifecycle.
• Mentor junior team members, participate in peer reviews, and promote analytical best practices.
• Evaluate emerging analytics, machine learning, and AI technologies that can improve business outcomes.
Required Education & Experience
Education
• Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, Data Science, Analytics, or a related quantitative discipline.
6 to 10+ Years of Experience
• Advanced analytics, statistical analysis, or data science solution delivery.
• SQL Server, T-SQL, data analysis, and relational data modeling.
• Python or R programming for analytics and model development.
1 to 3 Years of Experience
• Microsoft Fabric and analytics workloads.
• Python, PySpark, and notebook-based development using Jupyter or Marimo.
• Machine learning, feature engineering, model validation, and model monitoring.
• AI or Generative AI solution development.
• Medallion Data Architecture and Lakehouse concepts.
• Git-based version control and CI/CD practices for analytics projects.
Core Technical Skills
Advanced Analytics & Data Science
• Predictive and prescriptive analytics
• Statistical modeling, probability, and hypothesis testing
• Regression, classification, and clustering
• Forecasting and time-series analysis
• Segmentation, optimization, and anomaly detection
• A/B testing and experiment analysis
• Feature engineering and model evaluation
Technology & Tools
• Python, R, and SQL
• Microsoft Fabric and Power BI
• PySpark
• Jupyter and Marimo notebooks
• Git and version control
• Scikit-learn or equivalent machine learning frameworks
AI & Model Lifecycle
• Generative AI applications and prompt engineering
• Model deployment, monitoring, and lifecycle management
• Responsible AI practices
• Reusable analytics solution patterns and documentation
Strongly Preferred
• Experience building enterprise-scale analytics solutions and reusable analytical products.
• Experience with workforce, hiring, recruitment, financial, or operational analytics.
• Hands-on experience delivering machine learning and AI use cases into business processes.
• Experience presenting insights and recommendations to executive stakeholders.
• Understanding of MLOps concepts and end-to-end model lifecycle management.
• Experience using AI productivity tools such as GitHub Copilot and Microsoft Copilot.
• Microsoft Fabric, Power BI, Azure Data Scientist, or related certifications.
• Strong consulting, communication, problem-solving, and data storytelling skills.
Key Competencies
• Analytical and critical thinking
• Business acumen and problem framing
• Stakeholder collaboration
• Clear written and verbal communication
• Ownership and delivery focus
• Curiosity, continuous learning, and innovation
• Mentoring and knowledge sharing
Location : Chennai (WFO)
Experience : 7-10 Years
Shift : 2.00 PM to 11.00 PM (UK shift) and 5:30 PM to 2:30 AM (US Shift) – Rotational Basis
Key Responsibilities
• Partner with business stakeholders to understand business challenges, identify analytical opportunities, and translate requirements into scalable solutions.
• Design and develop advanced analytics models for forecasting, optimization, segmentation, classification, and predictive decision-making.
• Perform exploratory data analysis to identify patterns, trends, drivers, anomalies, and actionable business insights.
• Build, validate, deploy, and monitor machine learning and statistical models for enterprise business use cases.
• Develop analytical solutions using Python, R, SQL, Microsoft Fabric, notebooks, and related technologies.
• Create dashboards, scorecards, and self-service analytical experiences using Microsoft Power BI and Fabric.
• Collaborate with data engineers to define data requirements and ensure reliable, governed, analytics-ready datasets.
• Support AI and Generative AI initiatives by assessing business use cases and implementing appropriate analytical approaches.
• Build reusable analytics assets, feature engineering components, notebooks, templates, and frameworks.
• Document analytical methodologies, assumptions, model logic, validation results, limitations, and business recommendations.
• Present analytical insights and recommendations clearly to business and executive stakeholders.
• Apply data governance, privacy, security, and responsible AI standards throughout the analytics lifecycle.
• Mentor junior team members, participate in peer reviews, and promote analytical best practices.
• Evaluate emerging analytics, machine learning, and AI technologies that can improve business outcomes.
Required Education & Experience
Education
• Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, Data Science, Analytics, or a related quantitative discipline.
6 to 10+ Years of Experience
• Advanced analytics, statistical analysis, or data science solution delivery.
• SQL Server, T-SQL, data analysis, and relational data modeling.
• Python or R programming for analytics and model development.
1 to 3 Years of Experience
• Microsoft Fabric and analytics workloads.
• Python, PySpark, and notebook-based development using Jupyter or Marimo.
• Machine learning, feature engineering, model validation, and model monitoring.
• AI or Generative AI solution development.
• Medallion Data Architecture and Lakehouse concepts.
• Git-based version control and CI/CD practices for analytics projects.
Core Technical Skills
Advanced Analytics & Data Science
• Predictive and prescriptive analytics
• Statistical modeling, probability, and hypothesis testing
• Regression, classification, and clustering
• Forecasting and time-series analysis
• Segmentation, optimization, and anomaly detection
• A/B testing and experiment analysis
• Feature engineering and model evaluation
Technology & Tools
• Python, R, and SQL
• Microsoft Fabric and Power BI
• PySpark
• Jupyter and Marimo notebooks
• Git and version control
• Scikit-learn or equivalent machine learning frameworks
AI & Model Lifecycle
• Generative AI applications and prompt engineering
• Model deployment, monitoring, and lifecycle management
• Responsible AI practices
• Reusable analytics solution patterns and documentation
Strongly Preferred
• Experience building enterprise-scale analytics solutions and reusable analytical products.
• Experience with workforce, hiring, recruitment, financial, or operational analytics.
• Hands-on experience delivering machine learning and AI use cases into business processes.
• Experience presenting insights and recommendations to executive stakeholders.
• Understanding of MLOps concepts and end-to-end model lifecycle management.
• Experience using AI productivity tools such as GitHub Copilot and Microsoft Copilot.
• Microsoft Fabric, Power BI, Azure Data Scientist, or related certifications.
• Strong consulting, communication, problem-solving, and data storytelling skills.
Key Competencies
• Analytical and critical thinking
• Business acumen and problem framing
• Stakeholder collaboration
• Clear written and verbal communication
• Ownership and delivery focus
• Curiosity, continuous learning, and innovation
• Mentoring and knowledge sharing
Skills
- A/B Testing
- AI
- Analytics
- Anomaly Detection
- Azure
- CI/CD
- Data Governance
- Data Modeling
- Data Science
- Feature Engineering
- Generative AI
- Git
- GitHub
- Github Copilot
- Jupyter
- Lakehouse
- Machine Learning
- Microsoft Copilot
- Microsoft Fabric
- MLOps
- Model Deployment
- Model Evaluation
- Power BI
- Prompt Engineering
- PySpark
- Python
- scikit-learn
- SQL
- SQL Server
- Statistics
- Time Series
- Version Control