Data Scientist

US3 CONSULTING SERVICES LLC.,
United States
1 day ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours
Job source

Tech stack

Testing (Software) Clean Code Principles Automation of Tests Software Quality Continuous Delivery Continuous Integration Python (Programming Language) Machine Learning Tensorflow Software Engineering Pytorch Machine Learning Operations
+1 more
Docker

Job description

We’re looking for a Senior Data Scientist with strong Python engineering skills to design and develop high-quality, reusable libraries and packages that support machine learning workflows.

You’ll work closely with engineering and data science teams to build robust software, improve testing and CI/CD practices, and help create scalable, maintainable solutions for ML environments.

Key responsibilities

  • Design and develop high-quality, reusable Python libraries and packages.
  • Build maintainable software to support machine learning workflows.
  • Develop and maintain robust testing frameworks and automated tests.
  • Design, implement and enhance CI/CD pipelines.
  • Write clean, scalable and well-documented code.
  • Collaborate with data science and engineering teams to deliver ML-focused solutions.
  • Contribute to software quality, reliability and engineering best practices.
  • Support the development and optimisation of production-ready ML environments.

Requirements

  • Proven experience building and maintaining Python packages/libraries.
  • Strong software engineering practices and ability to write clean, maintainable code.
  • Hands-on experience with software testing, test automation and quality practices.
  • Experience designing and implementing CI/CD pipelines.
  • Strong understanding of software development lifecycle and engineering best practices.
  • Ability to collaborate effectively with data science and engineering teams.

Preferred skills

  • Experience with PyTorch or similar machine learning frameworks.
  • Understanding of GPU-accelerated workloads.
  • Exposure to speech technologies or speech-based machine learning.
  • Experience with Docker and Kubernetes.
  • Knowledge of observability, monitoring and production reliability practices.
  • Experience supporting or deploying machine learning workloads in production environments

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