Lead - Data Science

Azimuth Grc; Gurugram District; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Azimuth Grc in Gurugram seeks a senior data/ML engineer to lead end-to-end model development, build scalable data pipelines, and architect cloud ML solutions on Azure/AWS. You will develop synthetic data, ensure data validation, and contribute to governance and quality practices while collaborating in an agile environment with cross-functional teams.

You will leverage Databricks, Delta Lake, and MLflow to manage data and ML lifecycles, mentor team members, and create thorough technical

Qualifications

• Strong expertise in Python with Pandas, NumPy, Scikit-learn, and PySpark.
• Hands-on data engineering with ETL/ELT pipelines and data quality frameworks.
• Proven ML model development and deployment experience.
• Experience with cloud platforms (Azure/AWS) for ML solutions.
• Familiarity with Databricks ecosystem, including Delta Lake and MLflow.
• Knowledge of data validation, governance, and quality management practices.
• Excellent problem-solving, analytical, and communication skills.

Responsibilities

• Lead end-to-end model development lifecycle from data prep to evaluation and optimization.
• Design scalable ETL/ELT data pipelines ensuring quality.
• Architect and deploy cloud-based ML solutions on Azure and AWS.
• Develop synthetic data for testing and validation.
• Build data validation frameworks with schema checks.
• Drive process standardization and automation for repeatable workflows.
• Create and maintain technical documentation and knowledge bases.
• Operate within Agile frameworks (Scrum/Kanban) for delivery.
• Use Databricks, Delta Lake, and MLflow for lifecycle management.

Skills

Python Programming

Data Engineering

ML Model Deployment

Azure

AWS

Databricks

Delta Lake

MLflow

Pandas

NumPy

Scikit-learn

PySpark

Data Validation

Communication Skills

Tools

Databricks

Delta Lake

MLflow

Pandas

NumPy

PySpark

Job description

Key Responsibilities

• Lead End-To-End Model Development Lifecycle, including data preparation, model building, evaluation, and optimization
• Design and implement Scalable Data Pipelines (ETL/ELT) ensuring high data quality and reliability
• Architect and deploy Cloud-Based Machine Learning Solutions on Azure and AWS
• Develop Synthetic Data Solutions for testing and validation purposes
• Build and maintain Data Validation Frameworks, including schema validation and rule-based quality checks.
• Drive process standardization, automation, and optimization for repeatable workflows
• Create and maintain technical documentation and knowledge repositories
• Work within Agile frameworks (Scrum/Kanban) for efficient project delivery
• Leverage tools such as Databricks, Delta Lake, and MLflow for data and ML lifecycle management

Leadership & Stakeholder Management

• Lead, mentor, and coach a team of data scientists and engineers
• Own Sprint Planning, Backlog Management, and delivery timelines
• Collaborate with cross-functional teams including product, engineering, and business stakeholders
• Translating Business Requirements into Scalable Technical Solutions
• Present technical insights and architecture recommendations to senior leadership

Required Skills & Qualifications

• Strong expertise in Python Programming with libraries such as Pandas, NumPy, Scikit-learn, and PySpark
• Hands-on experience with data engineering concepts including ETL/ELT pipelines and data quality frameworks
• Proven experience in machine learning model development and deployment
• Experience with cloud platforms (Azure/AWS) for ML solutions
• Familiarity with Databricks ecosystem, including Delta Lake and MLflow
• Strong understanding of data validation, governance, and quality management practices
• Excellent problem-solving, analytical, and communication skills

Domain Expertise

• Experience in Finance and GRC (Governance, Risk, and Compliance) analytics
• Knowledge of risk modeling and regulatory compliance frameworks
• Understanding of AI governance and model lifecycle management