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

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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. ‍