[Remote] Senior Data Science and Machine Learning Engineer || Only W2

Note: The job is a remote job and is open to candidates in USA. Dice is seeking a Senior Data Science Engineer to design, build, and scale data-driven systems that power advanced analytics and machine learning across the organization. This role involves building robust data pipelines, enabling experimentation, and deploying production-ready machine learning models while mentoring junior engineers and influencing architectural decisions.

Responsibilities

• Develop, deploy, and maintain machine learning models in production environments
• Collaborate with data scientists, analysts, and product managers to define and deliver data-driven features
• Ensure high-quality data through monitoring, validation, and robust testing frameworks
• Architect and maintain data platforms and tools for experimentation, model serving, and feature engineering
• Explore and integrate Large Language Models (LLMs) and other generative AI approaches into business applications and data workflows
• Contribute to code reviews, technical design discussions, and best practices for the team
• Mentor and guide junior engineers/data scientists, fostering technical excellence and career growth
• Stay current with emerging technologies in Data Science, Machine Learning, LLM Ops, ML Ops

Skills

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field
• 5+ years of experience in data engineering, machine learning engineering, or related roles
• Strong proficiency in Python (Pandas, NumPy, PySpark, or similar)
• Solid understanding of ML model development, training, and deployment pipelines
• Experience with ML model monitoring and observability frameworks
• Experience with deep learning frameworks (TensorFlow, PyTorch)
• Familiarity with CI/CD, version control (Git), and modern ML Ops practices
• Master's degree or PhD is a strong plus
• Contributions to open-source Data Science / Machine Learning libraries or frameworks
• Hands-on experience with cloud platforms (AWS, Google Cloud Platform, or Azure) and containerization (Docker, Kubernetes)
• Proficiency with SQL and database systems (PostgreSQL, MySQL, or NoSQL alternatives)
• Exposure to data governance, security, and compliance requirements
• Knowledge of experiment design (A/B testing, causal inference)
• Strong problem-solving and analytical skills
• Excellent communication and collaboration abilities across technical and non-technical teams
• Leadership qualities and the ability to drive projects independently

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