Data Science Engineer - Machine Learning

Role: Data Science Engineer - Machine Learning

Client: Google Cloud PSO

Contract: 12-24 months, long-term

Location: San Jose, CA

Responsibilities:

Collaborate withproduct managers, data scientists, engineers, end users, and other stakeholdersto integrate data discoveries and processes into operational capabilities Be a data storyteller, deliver practical data insights in a compelling manner to seniorleadership. Articulate findings clearly and concisely includingpresentations, discussions and visualizations. Clear vision and understanding about implementing new AI Machine Learning frameworks against traditional programming models - Ability to develop complex algorithms in rolling out data centric application environment

Deeper understanding of mathematical models, regression analysis, clustering, bias and variance, decision trees, artificial neural networks, random forest

• • M.S. or Ph.D. in Computer Science or related technical field
• • Experience and interest in machine learning and AI, and in working with real world data sets
• • Production-quality developer in Python, C/C++, Java, or other general-purpose language
• • Fluent with software development best practices, including version control, documentation, testing and CI/CD
• • Experience with cloud infrastructure (AWS/Azure), particularly cloud storage (S3, Redshift) and computing (EC2, EMR)
• • Experience with production database systems (e.g. Postgres) and technologies
• • Experience with big data frameworks such as Cassandra, Spark and Hadoop
• • Familiar with data processing tools such as Apache Beam, AWS Data Pipeline and GCP Dataflow
• • Familiar with ML lifecycle tools such as MLflow, FBLearner Flow, TFX and Michelangelo
• • Demonstrated awareness of how to succeed in ambiguous circumstances
• • Experience with commercialization of analytics-driven applications / SaaS
• • Experience with analytics development for industrial applications in a commercial setting
• • Experience with virtualization and containerization, including managing and deploying containers using Kubernetes/Docker
• • Experience with frameworks for distributed orchestration of multiple workloads such as Airflow and Celery
• • Familiar with analytics frameworks and languages such as TensorFlow, Sci-kit learn, Spark, Scala, R, Python, MATLAB, etc.
• • System Engineering and API based integration experience for large production systems

Stellar IT Solutions is an Equal Opportunity Employer (EOE/AA)