Astro Sirens / Astro Sirens Staffing and Consulting

An IT staffing and consulting firm offering IT consulting, IT staffing, project management, CloudOps solutions, and DevOps transformation. It describes itself as providing top-notch IT talent and consulting services and matching clients with candidates for software development, network engineering, project management, and more.

IT services, staffing, and consulting

Description

  • Design, develop, and deploy machine learning models for real-world production use cases.
  • Analyze large and complex datasets to extract insights that inform model development and optimization.
  • Build end-to-end ML pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment.
  • Collaborate with data engineers, software engineers, product managers, and business stakeholders to define machine learning requirements.
  • Implement model monitoring, performance tracking, and retraining strategies for production models.
  • Optimize models for scalability, performance, and reliability in cloud-based environments.
  • Ensure data quality, reproducibility, and adherence to best practices in ML development.
  • Translate machine learning outcomes into clear, actionable insights for technical and non-technical audiences.
  • Contribute to improving ML standards, tools, and best practices across teams and mentor junior data scientists and ML engineers.

Requirements

  • Bachelor’s or Master’s degree in Data Science, Machine Learning, Computer Science, Statistics, or a related field.
  • 5+ years of experience in data science, machine learning, or applied AI roles.
  • Strong proficiency in Python for data processing and machine learning.
  • Hands-on experience with ML frameworks and libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).
  • Strong understanding of supervised and unsupervised learning, deep learning, and model evaluation techniques.
  • Expertise in SQL and experience with relational databases (PostgreSQL, MySQL, MS SQL).
  • Experience deploying machine learning models into production environments.
  • Familiarity with MLOps practices such as model versioning, CI/CD, monitoring, and retraining.
  • Experience with cloud platforms such as AWS, GCP, or Azure.
  • Understanding of data governance, model ethics, data privacy considerations, and strong communication skills to work with U.S.-based stakeholders.
  • Preferred: experience with big data technologies (Spark, Hadoop), Docker and Kubernetes/containerized ML workflows, and supporting ML systems at scale.

Benefits

  • Paid Time Off (PTO).
  • Work from home (remote position).
  • Professional development opportunities and training programs.
  • Collaborative and inclusive company culture.
  • Competitive salary and performance-based bonuses.

Interested in this position?

Apply directly on the company website

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