The Data Scientist / Analytics Consultant designs and delivers data analytics and machine learning solutions that support business decision‑making. This role translates business problems into data‑driven models and insights, contributes to client engagements, and supports solution design using cloud-native analytics, ML, and emerging GenAI capabilities across Microsoft Azure, AWS, and Google Cloud platforms.
- Design and deliver analytics and machine learning solutions aligned to business problems and client objectives
- Perform advanced exploratory data analysis (EDA), feature engineering, and data modeling
- Build, evaluate, optimize, and validate machine learning models
- Develop and maintain data pipelines, transformations, and model workflows
- Create insight-driven dashboards, reports, and analytical narratives for stakeholders
- Support solution design discussions, estimations, and technical documentation
- Collaborate with clients, business stakeholders, and delivery teams
- Ensure adherence to data governance, security, quality, and compliance standards
- Leverage cloud AI and GenAI tools responsibly to enhance analytics delivery
Skills:
- Strong analytical, statistical, and problem‑solving skills
- Hands-on applied machine learning experience
- Ability to translate business requirements into analytics solutions
- Experience in client-facing communication and stakeholder management
- Understanding of data governance, quality controls, and model validation
- Structured documentation and presentation skills
Tools and Technologies:
Microsoft Azure
Azure Machine Learning (training, experimentation, deployment)
Azure Data Factory / Azure Synapse / Microsoft Fabric (as applicable)
Azure SQL Database, Azure Data Lake
Power BI (semantic models, dashboards, optimization)
AWS
Amazon S3, AWS Glue (data ingestion & transformation)
AWS SageMaker (model development and experimentation – working knowledge)
Google Cloud (GCP)
BigQuery (analytics – working knowledge)
Vertex AI (model experimentation – exposure preferred)
Programming & Analytics
Python (scikit‑learn, pandas, numpy, statsmodels)
SQL
GenAI & AI Platforms
Azure OpenAI / Microsoft Copilot (analytics augmentation, summarization, insight generation)
Google Gemini (awareness / experimentation)
Anthropic Claude (assisted analytics, documentation, analysis use cases)
DevOps & Collaboration
Git (version control)
Azure DevOps (CI/CD pipelines, work item management)
Certifications needed:
- Microsoft Certified: Azure Data Scientist Associate (DP‑100)
Or
- AWS Certified Cloud Practitioner
Or
Google Cloud Digital Leader
