Data Science Engineer

TigerHire; Bengaluru; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

TigerHire in Bengaluru, India, seeks a data scientist to design, build, and deploy end-to-end ML models and pipelines in production. You will write complex SQL to extract and preprocess large enterprise datasets.

You will leverage AI-assisted tools like GitHub Copilot, collaborate with data engineering, software, and BI teams to integrate models, and monitor performance, drift, and bias with automated retraining workflows.

Qualifications

• Experience designing end-to-end ML solutions.
• Proficient in Python and SQL for data processing.
• Hands-on with model deployment and monitoring.
• Ability to mentor teammates and review code.

Responsibilities

• Design, build, and deploy end-to-end ML models and pipelines in production.
• Write complex SQL queries for data extraction and preprocessing.
• Leverage AI-assisted tools to boost productivity and quality.
• Collaborate with data engineering, software, and BI teams to integrate models.
• Monitor model health, drift, and performance with automated retraining.
• Conduct hypothesis testing, A/B testing, and statistical validation.
• Document architectures, data pipelines, experiments, and deployment steps.
• Mentor junior data scientists and promote coding standards.

Skills

Python

SQL

ML Engineering

Experimentation

Collaboration

Tools

GitHub Copilot

TensorFlow

PyTorch

Job description

• 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.