Senior Data Science and Machine Learning Engineer || Only W2

Senior Data Science and Machine Learning Engineer Fully Remote but candidate must be in EST or CST Duration : 12 Months Job Summary • We are seeking a Senior Data Science Engineer to design, build, and scale data-driven systems that power advanced analytics and machine learning across our organization. This role sits at the intersection of software engineering and data science; you’ll be responsible for building robust data pipelines, enabling experimentation, and deploying production-ready machine learning models. • As a senior team member, you will mentor junior engineers and data scientists, influence architectural decisions, and help shape the long-term AI and data strategy. Key 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. Education Requirement Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field. Master’s degree or PhD is a strong plus. Experience • 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. Nice-to-Have • Contributions to open-source Data Science / Machine Learning libraries or frameworks. • Hands-on experience with cloud platforms (AWS, GCP, 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). Soft Skills • 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.