As a Data Scientist you can expect to…

Perform data discovery, profiling, cleaning, and exploratory analysis across structured and semi-structured enterprise datasets
Develop predictive and statistical models that support forecasting, classification, segmentation, anomaly detection, and optimization use cases
Build and evaluate machine learning pipelines using Python-based frameworks within Azure analytics environments
Prepare feature sets and training datasets aligned with business objectives and model performance requirements
Partner with Data Engineers and Integration Engineers to deploy models into production environments using Azure-based platforms
Support ingestion and preparation of data for analytics and intelligent applications across Microsoft Fabric, Synapse, and Azure Databricks
Contribute to solutions supporting Copilot, Azure OpenAI, semantic search, and retrieval-based intelligent application scenarios
Design and execute experiments to validate model performance and improve prediction accuracy
Translate analytical outputs into clear business insights and recommendations for technical and functional stakeholders
Document modeling assumptions, feature engineering approaches, evaluation results, and deployment workflows
Support model monitoring activities including drift detection, retraining strategies, and performance tracking
Ensure alignment with enterprise governance, data security, and responsible AI practices across engagements
Stay current with emerging machine learning, analytics, and generative AI technologies within the Microsoft ecosystem

You’re great at…

Handling multiple projects/tasks simultaneously  in a team environment
Applying statistical analysis and machine learning techniques to solve business problems using real-world enterprise datasets
Building predictive models using methods such as regression, classification, clustering, and time-series forecasting
Preparing and transforming datasets for modeling through feature engineering and exploratory data analysis
Developing machine learning workflows using Python and common ML libraries such as scikit-learn, TensorFlow, or PyTorch
Supporting analytics environments such as Microsoft Fabric, Azure Databricks, Synapse, and Power BI data models
Collaborating with Data Engineers and Integration Engineers to operationalize models within production data platforms
Designing experiments to evaluate model effectiveness and improve prediction accuracy over time
Translating analytical findings into actionable insights for both technical and business stakeholders
Working with cloud-based analytics and machine learning services, particularly within Microsoft Azure environments
Documenting modeling logic, assumptions, and outputs to support maintainability and governance requirements
Supporting feature engineering and model deployment within production data platforms
Promoting the mission and Shared Values of our company

Sound interesting? If so, you’ll have…

Bachelor’s degree in Computer Science, Information Technology, Statistics, Mathematics, or a related field
Strong proficiency in Python and SQL for data preparation, modeling, and analysis workflows
Development of predictive models using techniques such as regression, classification, clustering, or forecasting
Familiarity with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or similar frameworks
Support of analytics or modern data platforms such as Microsoft Fabric, Azure Databricks, Synapse, or Power BI
Work within cloud-based analytics or machine learning environments, preferably Microsoft Azure
Exposure to model deployment workflows and collaboration with engineering teams supporting production implementations
Clear communication of analytical insights to both technical and non-technical audiences
Ability to collaborate effectively across cross-functional delivery teams in consulting or enterprise solution environments
Microsoft Azure certifications related to data, analytics, or AI platforms preferred
Ability to work with clients and internal teams to deliver solutions

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