We are looking for a seasoned Data Science Engineer to join our Advanced Analytics team. In this role, you will be responsible for designing, developing, and optimizing scalable data science solutions and machine learning pipelines. Your work will support the deployment, monitoring, and performance tuning of models in production environments.

You’ll collaborate closely with data scientists, mobile core engineers, and application developers to deliver actionable insights through advanced network monitoring and troubleshooting platforms.
 

You have:

8+ years of experience in data science, machine learning engineering, or ML Ops roles.Advanced proficiency in Python and key ML/analytics libraries such as scikit-learn, TensorFlow, PyTorch, pandas, and NumPy.Hands-on experience with ML Ops tools and platforms like MLflow, Kubeflow, SageMaker, Vertex AI, and Airflow.Deep expertise in deploying, monitoring, and maintaining machine learning models in production environments.Strong background in data analytics and big data processing using technologies such as Spark and Hadoop, along with proficiency in SQL and NoSQL databases.Experience working with major cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes). Familiarity with CI/CD pipelines, Git, and DevOps best practices.

It would be nice if you also had:

Experience with data visualization tools such as Tableau, Power BI, or Plotly.Knowledge of data privacy, security, and compliance considerations in ML and analytics workflows.

P.S: This is an onsite position based in Sunnyvale, California. Visa sponsorship and relocation assistance are not available for this role.

Architect, develop, and maintain end-to-end ML Ops pipelines to support the full machine learning lifecycle, including model training, validation, deployment, monitoring, and retraining.Collaborate cross-functionally with data scientists and engineers to operationalize machine learning models and integrate them seamlessly into network applications.Design and build scalable analytics solutions using big data technologies such as Spark, Hadoop, and Databricks.Automate data workflows, including ingestion, transformation, and feature engineering, to streamline model development and deployment.Monitor model performance and system health, proactively detecting data drift and anomalies using modern observability and alerting tools.Champion CI/CD methodologies for ML and analytics projects, incorporating automated testing, validation, and deployment processes.Optimize infrastructure for performance, cost-efficiency, and reliability across both cloud and on-premises environments.