About the role
QT-SNB-10-395
Data ScientistExperience: 6–8 yearsLocation-Gurugram, Bengaluru , Pune , Jaipur, New Delhi , Noida , Hyderabad , Chennai , Bhopal , Mumbai
Role OverviewWe 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 ResponsibilitiesModel 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.