Data Scientist

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.

● Experience with real-time or batch inference systems.

● Exposure to CI/CD for ML pipelines.

● Familiarity with model monitoring, drift detection, and retraining strategies.