Data Scientist

QT-SNB-10-395

Data Scientist

Experience: 6–8 years

Location-Gurugram, Bengaluru , Pune , Jaipur, New Delhi , Noida , Hyderabad , Chennai , Bhopal , Mumbai

Role Overview

We are seeking a Data Scientist with a strong engineering background to design, build, train, and operationalize machine learning models that deliver measurable business impact. You will work end-to-end across feature engineering, model training, inference, and post-processing, leveraging a modern, cloud-native ML platform built on GCP and Kubernetes. This role blends strong statistical and machine learning expertise with hands-on MLOps practices, ensuring models are reliable, scalable, and production-ready.

Key Responsibilities

Model Development & Data Science

• Develop, train, and validate machine learning models using Python.
• Perform feature engineering, exploratory data analysis, and model evaluation.
• Apply appropriate ML techniques for prediction, classification, or optimization use cases. ML Workflow Orchestration
• Use Argo Workflows on Kubernetes to orchestrate model inference and post-processing pipelines.
• Design repeatable, automated workflows for ML experiments and production inference. Model Training & Validation
• Leverage Vertex AI to run scalable model training, hyperparameter tuning, and validation.
• Ensure reproducibility and consistency across training runs. Model Runtime & Inference
• Build and maintain Python-based runtimes for training, inference, and feature engineering.
• Optimize inference pipelines for performance, reliability, and scalability.
• Handle and monitor pipelines in production environments..
• Participate in Oncall rotation for inference infrastructure. Model Storage & Lifecycle Management
• Handle trained artifacts in Google Cloud Storage (GCS).
• Track model metadata, versions, and lineage using Argo or custom model registries.
• Support model versioning, rollback, and auditability.. Quality, Monitoring & Governance
• Define model evaluation metrics and validation criteria.
• Support post-deployment monitoring, drift detection, and retraining strategies.
• Follow best practices for documentation, testing, and responsible AI usage.

Required Skills & Qualifications Technical Skills

• Strong on-hands proficiency in Python for data science and machine learning use-cases.
• Hands-on experience with ML frameworks (e.g., PyTorch, scikit-learn, catboost, …).
• Familiarity with Kubernetes and Kubernetes-based workflows, especially Argo Workflows.

Data Science & ML Concepts

• Strong understanding of feature engineering, model evaluation, and validation techniques.
• Experience taking models from experimentation to production inference.
• Understanding of ML lifecycle management and MLOps principles. Soft Skills
• Strong analytical and problem-solving mindset.
• Ability to translate business problems into data science solutions.
• Clear communication skills to explain models and results to diverse stakeholders.

Nice to Have

• Experience with real-time or batch inference systems.
• Exposure to CI/CD for ML pipelines.
• Familiarity with model monitoring, drift detection, and retraining strategies.