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.
