I&IT Data Scientist I

• Design, develop, and maintain scalable data pipelines and analytical solutions using Microsoft Azure, Databricks Lakehouse, Azure AI Foundry, Unity Catalog, Delta Lake, and Azure Data Lake Storage to support enterprise analytics, artificial intelligence, and machine learning initiatives. 

• Build, curate, and manage trusted datasets through data ingestion, transformation, integration, and quality validation processes using SQL, Python, PySpark, and Databricks. 

• Perform exploratory data analysis (EDA) to identify trends, patterns, anomalies, and opportunities that support business and operational decision-making. 

• Develop, validate, deploy, and monitor machine learning and AI models using regression, classification, clustering, forecasting, and generative AI techniques to solve business challenges. 

• Leverage Azure AI Foundry and Azure AI services to develop, evaluate, and operationalize AI applications, including generative AI, intelligent agents, and retrieval-augmented solutions.

• Collaborate with business stakeholders to understand requirements, define success metrics, and translate business problems into scalable data science, analytics, and AI solutions. 

• Partner with Data Engineers, AI Engineers, and Solution Architects to operationalize machine learning and AI solutions through MLOps and LLMOps best practices, including model deployment, monitoring, retraining, and lifecycle management. 

• Extract, integrate, and analyze large-scale enterprise data from Azure cloud platforms, Databricks environments, and operational systems using distributed computing frameworks. 

• Develop reusable notebooks, feature engineering pipelines, semantic models, and AI assets that improve the scalability and consistency of enterprise analytics and AI capabilities. 

• Apply advanced statistical techniques, machine learning algorithms, and experimentation methodologies to evaluate business opportunities, measure outcomes, and quantify business value.

• Support the implementation and continuous improvement of enterprise AI, machine learning, and analytics platforms, standards, and best practices. 

• Ensure compliance with enterprise data governance, privacy, security, responsible AI, and model risk management requirements. 

• Contribute to the advancement of the Enterprise AI & Data CoE by evaluating emerging technologies and identifying opportunities to enhance analytical, machine learning, and AI capabilities across the organization. 

• Communicate analytical findings, model performance, and AI solution outcomes effectively to both technical and non-technical stakeholders. 

• Collaborate in an Agile delivery environment to support enterprise-wide data, analytics, automation, and AI initiatives.

   

Education: Completion of a Degree in Statistics, Applied Math, Engineering, Computer Science, or a related discipline – or a combination of education, training, and experience deemed equivalent

Experience:

• Demonstrated experience in Data Science, Advanced Analytics, Machine Learning, Predictive Modeling, or related disciplines.

• Proficiency in Python, SQL, PySpark/Spark, and large-scale data processing.

• Hands-on experience with Microsoft Azure, Azure Machine Learning, Databricks Lakehouse, Microsoft Fabric, Azure Data Lake Storage, and Azure AI services.

• Experience developing and deploying machine learning, forecasting, predictive, and prescriptive analytics models.

• Strong knowledge of statistical analysis, including hypothesis testing, experimentation, and A/B testing.

• Experience with data engineering, ETL/ELT pipelines, feature engineering, and data quality management.

• Demonstrated applied machine learning experience, including time-series forecasting.

• Experience with customer segmentation, profiling, clustering, association analysis, and basket analysis.

• Knowledge of MLOps, LLMOps, Generative AI, RAG, and AI Agent frameworks is an asset.

• Experience working with complex enterprise datasets and distributed computing environments.