Data Scientist

Location: Gurgaon, Jaipur, Pune, Bangalore, Chennai, Hyderabad, Bhopal
Experience: 68 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 ana lysis, 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.
- Transparent 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.

📌 Data Scientist (Hyderabad)
🏢 Xebia IT Architects
📍 Hyderabad