Xebia - Data Scientist - Python

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 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.

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.

(ref:hirist.tech)