Data Science Engineer

Job Description

• We are looking for a skilled Data Science Engineer with 6 to 9 years of experience to join our growing analytics and AI team in Bengaluru, driving end-to-end machine learning solutions from data ingestion to model deployment.
• The ideal candidate will have deep expertise in Machine Learning, including supervised and unsupervised learning, model evaluation, feature engineering, and productionizing ML pipelines at scale.
• Proficiency in Python is essential, including hands-on experience with libraries such as scikit-learn, pandas, NumPy, TensorFlow or PyTorch, and building modular, production-ready code.
• Strong SQL skills are required to extract, transform, and analyze large datasets from relational databases, supporting data preparation and model validation workflows.
• Experience using GitHub Copilot and AI-assisted coding tools to accelerate development cycles, improve code quality, and streamline repetitive engineering tasks is highly valued.
• Candidates must be comfortable working in a collaborative, agile environment in Bengaluru, partnering with data engineers, product managers, and business stakeholders to translate complex data problems into actionable ML solutions.
• You will be expected to design and maintain robust data science workflows including model monitoring, retraining pipelines, and performance dashboards to ensure sustained model accuracy in production.
• A strong foundation in statistical modeling, experimentation frameworks such as A/B testing, and data storytelling to communicate insights clearly to both technical and non-technical audiences is required.

Roles & Responsibilities

Job Roles And Responsibilities

• Design, build, and deploy end-to-end machine learning models and pipelines using Python and modern ML frameworks, ensuring scalability and reliability in production environments.
• Write complex SQL queries to extract and preprocess large volumes of structured data from enterprise databases, enabling high-quality feature engineering and exploratory data analysis.
• Leverage GitHub Copilot and other AI-assisted development tools to enhance coding productivity, enforce best practices, and reduce time-to-delivery for data science solutions.
• Collaborate with cross-functional teams including data engineering, software development, and business intelligence to integrate ML models into existing products and data platforms.
• Monitor deployed models for data drift, performance degradation, and bias, implementing automated retraining workflows and alerting mechanisms to maintain model health over time.
• Conduct rigorous experimentation including hypothesis testing, A/B testing, and statistical validation to measure the real-world impact of ML-driven features and recommendations.
• Document model architectures, data pipelines, experimental findings, and deployment processes to ensure reproducibility and knowledge sharing across the team.
• Mentor junior data scientists and contribute to the team's technical growth by conducting code reviews, establishing coding standards, and promoting best practices in ML engineering.