Data Scientist

Qloron Pvt Ltd; Pune District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Qloron Pvt Ltd in Pune, India is seeking a Data Scientist to design, build, train, and operationalize ML models delivering measurable business impact. You will work end-to-end across feature engineering, model training, inference, and post-processing on a cloud-native ML platform built on GCP and Kubernetes.

You will develop, train, and validate models, perform EDA, and apply ML techniques for prediction, classification, or optimization, while integrating ML workflows with Argo and Vertex AI for

Qualifications

• Proficient in Python for data science and ML tasks.
• Hands-on experience with ML frameworks (PyTorch, scikit-learn, CatBoost).
• Familiarity with Kubernetes and Argo Workflows.

Responsibilities

• Develop, train, and validate ML models using Python.
• Perform feature engineering, EDA, and model evaluation.
• Apply ML techniques for prediction, classification, or optimization; orchestrate ML workflows with Argo on Kubernetes.

Skills

Python

ML frameworks

Kubernetes

Argo Workflows

Vertex AI

MLOps

Tools

Vertex AI

Google Cloud Storage (GCS)

Argo

Job description

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

• 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.