Job Description:
Title: Senior Data Science and Machine Learning Engineer
Location: Remote , EST
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 will 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
Bachelors degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
Masters 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, 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).
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.
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