Role : Data Scientist

Location : Gurgaon, Jaipur, Pune, Bangalore, Chennai, Hyderabad, Bhopal

Experience : 6 to 8 years

Role Overview :

We are seeking a Data Scientist 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 up-to-date, 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.

Model Storage &

- Lifecycle Management :
- Store trained models and artifacts in Google Cloud Storage (GCS).
- Track model metadata, versions, and lineage using Argo or custom model registries.
- Support model versioning, rollback,



and auditability.

Production Deployment &

- Collaboration :
- Partner with data engineers and platform teams to integrate models into production systems.
- Ensure smooth handoff from experimentation to deployment.
- Participate in design discussions around ML architecture and best practices.

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 proficiency in Python for data science and machine learning.
- Hands-on experience with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
- Experience running ML workflows on Vertex AI or similar managed ML platforms.
- Familiarity with Kubernetes-based workflows, especially Argo Workflows.
- Experience managing model artifacts and metadata in GCS or equivalent object storage.

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.
- Experience working in cloud-native or regulated environments.

📌 Xebia - Data Scientist - Python (India)
🏢 Xebia
📍 India