Manager - Data Sciences

LTM

$90K — $130K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years experience in machine learning operations and deployment
  • Proficient in AWS or similar cloud platforms
  • Skilled in Docker, Jenkins, Kubernetes, and other DevOps tools
  • Familiarity with Kubeflow or MLflow for model management
  • Expertise in Python and machine learning libraries
  • Experience using version control tools like Git and Bitbucket
  • Strong SQL knowledge and database management skills

Responsibilities

  • Own the operationalization and monitoring of machine learning models
  • Ensure data science quality assurance and robust testing protocols
  • Monitor and troubleshoot ML models in production environments
  • Build and maintain scalable Python libraries for production use
  • Implement best practices for version control and CI/CD
  • Collaborate with cross-functional teams, including data scientists and engineers
  • Adapt quickly in a fast-paced, collaborative work environment

Benefits

  • Flexible work hours to promote work-life balance
  • Opportunities for professional development and training
  • Access to cutting-edge tools and technologies
  • Participation in a diverse and inclusive team culture
  • Health and wellness benefits
Full Job Description
Role description

Client Interview Expectations

Job Description ML Engineer Job Summary Were looking for a Machine Learning Engineer who is experienced in deploying and managing machine learning models at scale in production for our ML Operations team The team is a cross functional team and has Software Engineers and DS Engineers and closely works with data scientists and data engineers in operationalizing ML models Roles and Responsibilities As an ML Engineer in the MLOps team you will be a key stakeholder and owning responsibility in operationalize and monitor machine learning models using high end tools and technologies Data Science quality assurance and testing Monitoring and troubleshooting of in production ML models and tools Build and maintain production level python libraries Execute best practices in version control and continuous integration delivery Collaborate with data scientists engineers and other key stakeholders Work well in a fast paced cross functional environment Skills Requirements Experience in implementing machine learning life cycle on AWS or other cloud platforms Knowledge on Docker Jenkins Kubernetes and other DevOps tools Familiarity with Kubeflow or mlflow Experience in Python Experience with Machine learning frameworks libraries and agile environments Experience with version control tools such as Git Bitbucket etc Experience with SQL and databases Outstanding analytical and problem solving skills

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