Senior Data Science and Machine Learning Engineer

Senior Data Science and Machine Learning Engineer

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.

‍

Qualifications:

 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.

‍