Data Scientist

Role Overview

We are looking for an experienced Data Scientist with a robust engineering background and hands-on expertise in Machine Learning and MLOps.

The candidate will be responsible for designing, developing, training, and deploying machine learning models into production environments. The role requires strong Python and ML expertise, along with practical experience in GCP, Kubernetes, Vertex AI, and Argo Workflows.

Key Responsibilities

1. Model Development & Data Science

- Develop, train, and validate machine learning models using Python.
- Perform feature engineering, exploratory data analysis (EDA), and model evaluation.
- Apply ML techniques for prediction, classification, and optimization use cases.

2. ML Workflow Orchestration

- Use Argo Workflows on Kubernetes to orchestrate model inference and post-processing pipelines.
- Build automated and repeatable workflows for ML experiments and production inference.

3. Model Training & Validation

- Leverage Google Vertex AI for scalable model training, hyperparameter tuning, and validation.
- Ensure reproducibility and consistency across training runs.

4. Model Runtime & Inference

- Build and maintain Python-based runtimes for model training, inference, and feature engineering.
- Optimize inference pipelines for performance, reliability, and scalability.
- Monitor and manage production ML pipelines.
- Participate in on-call rotations for inference infrastructure.

5. Model Storage & Lifecycle Management

- Manage trained model artifacts using Google Cloud Storage (GCS).




- Track model metadata, versions, and lineage using Argo or custom model registries.
- Support model versioning, rollback, and auditability.

6. 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.

Required Skills & Qualifications

Technical Skills

- Strong hands-on proficiency in Python for Data Science and Machine Learning.
- Experience with ML frameworks such as PyTorch, Scikit-learn, and CatBoost.
- Familiarity with Kubernetes and Kubernetes-based workflows.
- Hands-on experience with Argo Workflows.

Data Science & ML Concepts

- Strong understanding of feature engineering, model evaluation, and validation techniques.
- Experience taking ML models from experimentation to production inference.
- Understanding of ML lifecycle management and MLOps principles.

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 automated retraining strategies.

Soft Skills

- Strong analytical and problem-solving abilities.
- Ability to translate business problems into data science solutions.
- Excellent communication and stakeholder management skills.

Pay: ₹90,000.00 - ₹110,000.00 per year

Experience:

- data engineer: 6 years (Required)

Work Location: Remote

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