Data Scientist

Data Scientist

Location: Hyderabad (Hybrid)

Experience: 68 years

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.