Data Science Engineer

Infovision Labs; Bengaluru; Hybrid; INR 2,400,000 - 4,200,000; Full time

Job summary

Infovision Labs in Bengaluru is seeking a Data Science Engineer with 6–9 years of experience to drive end-to-end ML solutions from data ingestion to deployment.

The role emphasizes productionizing ML pipelines, strong Python and SQL, and collaboration in an agile environment with data engineers, product managers, and stakeholders.

You will design robust workflows, monitor models, and communicate insights clearly to technical and non-technical audiences.

Qualifications

• 6–9 years of experience in data science or ML engineering.
• Proficiency in Python with scikit-learn, NumPy, pandas.
• Strong SQL skills for large-scale data extraction and prep.
• Experience deploying ML models in production and building scalable pipelines.
• Comfortable in agile, cross-functional collaboration.

Responsibilities

• Design, build, and deploy end-to-end ML models and pipelines.
• Write complex SQL queries to preprocess large datasets.
• Leverage AI-assisted tools to boost coding productivity.
• Collaborate with data engineering, software, and BI teams.
• Monitor models for drift and retraining needs.
• Run experiments with hypothesis testing and A/B tests.
• Document architectures, pipelines, and deployment steps.
• Mentor junior data scientists and promote ML best practices.

Skills

Machine Learning

Model evaluation

Feature engineering

Production ML pipelines

Python

SQL

GitHub Copilot

AI tools

Agile

Data storytelling

Tools

TensorFlow

PyTorch

scikit-learn

Pandas

NumPy

Job description

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 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.