Design, build, and enhance data-driven systems and analytical models supporting an Azure based AI and agentic platform. The Data Scientist applies mathematical, statistical, and data engineering expertise to develop robust data pipelines, enable model training, and ensure continuous improvement of AI performance and reliability.

Responsibilities:

Build, enhance, and maintain data pipelines using Azure Data Factory, Synapse Analytics, and Databricks.
Ingest, explore, and preprocess structured and unstructured data from diverse sources.
Ensure data quality, transformation, and governance across ingestion and storage layers.
Optimize data storage and retrieval within Azure Data Lake Storage and Blob Storage.
Perform exploratory data analysis (EDA) and statistical profiling using Azure Machine Learning Notebooks or Azure Databricks.
Assess data quality, detect anomalies, and recommend data cleansing and enrichment strategies.
Collaborate closely with Machine Learning Engineers and Solution Architects to prepare, deploy, and monitor models in production environments.
Design and implement automated retraining and monitoring pipelines to detect model drift and ensure sustained accuracy.
Ensure reproducibility of experiments and maintain version control for models, datasets, and scripts.
Develop and maintain data documentation and metadata catalogs to ensure transparency and reusability.
Support data-driven decision-making by building analytical datasets, KPIs, and performance dashboards.

Qualifications

Bachelor s or Master s degree in Mathematics, Statistics, Computer Science, or Data Science.
Proven experience in data pipeline design and development using Azure Data Factory, Synapse, or Databricks.
Strong background in mathematics, probability, and statistical modeling.
Proficiency in Python (Pandas, NumPy, PySpark, scikit-learn) for data wrangling and analytics.
Hands-on experience with Azure Data Lake, Blob Storage, and Azure Machine Learning.
Familiarity with ML model deployment workflows and performance monitoring.
Experience with data versioning, experiment tracking, and reproducibility frameworks (e.g., MLflow, DVC).
Strong problem-solving and analytical skills with a focus on scalability and data quality.
Knowledge of data governance, privacy standards, and compliance best practices.

Disclaimer: The job location mentioned in this description is based on publicly available information or company headquarters. Candidates are advised to verify the exact job location directly with the employer before applying.

Data Scientist Azure AI Platform

Job Description

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