Lead conceptual designs and create frameworks to predict a variety of outcomes in different scenarios
Build, deploy, and optimize machine learning models
Collaborate with other data scientists and stakeholders on projects
Develop solutions in Python
Develop production-grade solutions
Work in Hadoop, Redshift, and Spark
Translate business and product questions into analytics projects
Communicate clearly over written and oral channels

Requirements

10-15 years of experience as a machine learning (MLOps), data, and/or software engineer using Python in a production environment
Experience building, deploying, and optimizing machine learning models
Strong experience with Azure ML and Databricks/MLflow
Strong experience with Terraform and Kubernetes
Experience building and managing robust CI/CD pipelines for machine learning workflows
Knowledge of professional enterprise software development and practices, including software lifecycle, version control, architecture, testing, and deployment
Familiarity with popular machine learning libraries and frameworks, including TensorFlow, Keras
Experience with GitHub
Experience building data pipelines

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningPythonHadoopRedshiftSparkAzure MLDatabricksMLflowTerraformKubernetes

Soft Skills

collaborationcommunication