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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


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)

Please email your resume to hr@stellaritgroup.com along with your current location and type of work authorization you have to work in USA.