Senior Data Scientist - Remote

Pivotal Solutions Inc
yesterday

Role details

Contract type
Contract
Employment type
Part-time (≤ 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Remote

Tech stack

Continuous Integration
Data Systems
Data Warehousing
Cursor (Graphical User Interface Elements)
Github
Monitoring of Systems
Python
Machine Learning
Datadog
Feature Engineering
Data Ingestion
Delivery Pipeline
Snowflake
GIT
Kubernetes
Machine Learning Operations
Software Version Control
Docker

Job description

  • Design, develop, and productionize robust machine learning pipelines in a fast-paced environment.
  • Collaborate closely with data scientists, engineers, and cross-functional teams to translate models into reliable, scalable production systems.
  • Own end-to-end aspects of the ML lifecycle, including data ingestion, feature engineering, model training, deployment, monitoring, and iteration.
  • Implement and maintain CI/CD workflows for ML systems to ensure rapid, reliable releases.
  • Troubleshoot and optimize complex pipeline issues to meet aggressive timelines.
  • Contribute to a positive, high-performing team culture through clear communication, knowledge sharing, and proactive problem-solving.

Requirements

This is a 6+ month remote contractor position. Our client is seeking a talented and motivated Senior Data Scientist (ML) to join our team on a high-impact project with tight deadlines and a complex ML pipeline. This is an excellent opportunity for a strong technical contributor who thrives in a collaborative environment and can deliver results under pressure. You will play a key role in building, optimizing, and maintaining scalable machine learning systems that drive real business value., * Strong Python expertise with hands-on experience building production-grade ML applications.

  • Proven ability to work effectively in a team setting with tight deadlines and complex technical challenges.
  • Solid understanding of machine learning operations (MLOps) best practices.
  • Experience with containerization using Docker.
  • Proficiency with Git and version control workflows, including GitHub Actions.
  • Strong problem-solving skills and a collaborative, team-first mindset., * Hands-on experience with Kubeflow for orchestrating ML workflows.
  • Familiarity with Snowflake for data warehousing and analytics.
  • Experience with observability and monitoring tools, particularly Datadog.
  • Background in building and managing CI/CD pipelines for ML or data systems.
  • Experience using AI-assisted development tools such as Windsurf, Devin, Cursor, or Claude Code to accelerate development.

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