Senior Data Scientist / Machine Learning Engineer
Astro Sirens
- United States
- United States
Über
We are currently seeking a
Senior Data Scientist / Machine Learning Engineer
to help our clients design, build, and deploy scalable machine learning solutions that drive business value. This is a remote position, and we strongly encourage and give preference to candidates who are eager to collaborate with U.S.-based teams.
Responsibilities
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
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
Mentor junior data scientists and machine learning engineers
Qualifications
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 machine learning 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 (model versioning, CI/CD, monitoring, retraining)
Experience with cloud platforms such as AWS, GCP, or Azure
Understanding of data governance, model ethics, and data privacy considerations
Strong communication skills with the ability to work effectively with U.S.-based stakeholders
Preferred Qualifications
Experience with big data technologies (Spark, Hadoop, or similar)
Knowledge of Docker, Kubernetes, and containerized ML workflows
Experience supporting ML systems at scale
Benefits
Paid Time Off (PTO)
Work From Home
Professional development opportunities
Training & Development Programs
Collaborative and inclusive company culture
Competitive salary and performance‑based bonuses
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Sprachkenntnisse
- English
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