Senior Data Scientist- Azure & ML Specialist || Onsite- Pleasanton, CA (Relocation; Ok)

DMS Vision • Frisco, TX

11h • Onsite

About The Position

We seek a hands-on Senior Data Scientist to build, deploy, and optimize machine-learning models at scale. The role focuses on Python- and SQL-driven data pipelines, machine-learning and deep-learning modeling, visualization, and cloud-based production deployment, with a strong emphasis on the Azure ecosystem for enterprise data workflows. The position is onsite in Pleasanton, California; relocation is acceptable. The posting describes a duration of 6+ months.

Requirements

• 10+ years delivering end-to-end machine-learning solutions.
• Strong hands-on Python skills for data preparation, modeling, and machine-learning components.
• SQL skills, including joins, window functions, CTEs, and query optimization.
• Experience with linear and logistic regression, decision trees, random forests, XGBoost or LightGBM, SVM, and KNN.
• Knowledge of model evaluation metrics including precision, recall, F1, ROC-AUC, MSE, and RMSE, and tuning methods including grid search, randomized search, and cross-validation.
• Deep-learning experience with TensorFlow, Keras, or PyTorch, including CNNs, RNNs/LSTMs, and Transformers, applied to NLP, computer vision, or time-series use cases.
• Data-wrangling skills, including missing-data handling, feature engineering, data cleaning, outlier detection, and normalization or standardization.
• Data-visualization experience with Python libraries such as Matplotlib, Seaborn, or Plotly, and Tableau or Power BI.
• Experience with relational databases such as MySQL, PostgreSQL, or SQL Server, and NoSQL databases such as MongoDB or Cassandra.

Nice To Haves

• Big-data experience with Spark or Hadoop.
• Experience with a cloud platform; Azure is strongly preferred, including Data Factory, Databricks, ML Studio, or Synapse Analytics. AWS or GCP experience is also listed.
• PySpark on Databricks.
• Deployment experience with Flask or FastAPI; Docker and Kubernetes are optional.
• Basic CI/CD knowledge.
• Experience with Airflow or Prefect for data pipelines; these are optional.

Responsibilities

• Build, deploy, and optimize machine-learning models at scale.
• Develop and work with Python- and SQL-driven data pipelines.
• Perform machine-learning and deep-learning modeling.
• Create visualizations, dashboards, reports, and data storytelling.
• Deploy models to cloud-based production environments, with a strong emphasis on Azure.
• Prepare and clean data, including feature engineering and handling missing data and outliers.

What This Job Offers

• Career Level: Senior
• Education Level: No Education Listed