The Data Science Lead / Analytics Lead owns end‑to‑end analytics and machine learning solution architecture and leads complex, enterprise‑scale data science engagements. This role defines best practices, reference architectures, and governance standards, ensures scalable and compliant delivery, and acts as a technical and thought leader for analytics initiatives across clients and internal teams.
- Own data science solution architecture, technical design, and delivery approach
- Lead analytics and ML delivery across multiple workstreams and teams
- Define standards for data pipelines, feature engineering, model lifecycle, and MLOps
- Design scalable enterprise data platforms and analytics foundations
- Ensure model performance, explainability, monitoring, and governance
- Lead client workshops, architectural discussions, solution reviews, and analytics demos
- Guide adoption of cloud AI and GenAI capabilities aligned to business value and compliance
- Identify delivery risks and ensure quality, security, and regulatory adherence
- Mentor consultants and associates, conduct technical reviews, and build analytics capability
Tools and Technologies:
Microsoft Azure (Primary)
Azure Machine Learning (enterprise ML lifecycle, MLOps, deployment patterns)
Azure Data Platform: Azure Synapse / Microsoft Fabric / Azure Data Lake
Power BI (enterprise semantic models, governed analytics, performance optimization)
Azure Integration Services (Functions, Logic Apps – as required)
AWS
Amazon S3, AWS Glue (enterprise ingestion and transformation patterns)
AWS SageMaker (ML architecture, experimentation, and deployment – advanced understanding)
Google Cloud (GCP)
BigQuery (enterprise analytics design – working knowledge)
Vertex AI (ML and GenAI architecture exposure)
Programming & Engineering
+F2
Python (advanced analytics, ML, and framework integration)
SQL (complex transformations and performance optimization)
GenAI & AI Platforms
Azure OpenAI / Microsoft Copilot (enterprise GenAI integration, guardrails, and enablement)
Google Gemini (GenAI architecture awareness and experimentation)
Anthropic Claude (enterprise usage considerations for analysis and documentation)
DevOps & MLOps
CI/CD pipelines using Azure DevOps
Model versioning, monitoring, and release governance
Skills Needed
- Strong data architecture and machine learning design expertise
- Deep understanding of enterprise analytics patterns and platforms
- Experience establishing ML governance, explainability, and compliance models
- Proven technical leadership and mentoring capability
- Excellent stakeholder, client, and risk management skills
- Ability to balance innovation with scalability and control
Certifications Need -
- Microsoft Certified: Azure Data Scientist Associate (DP‑100)
Or
AWS Certified Machine Learning
Or
Google Professional Data Engineer
