Data Scientist - Machine Learning

Job Role: Data Scientist

Work mode : Hybrid(Pune/Bengaluru/Gurugram)

Total experience : 6+yrs

Requirements :

- Total experience 6+ years.

- Strong working experience in Python.

- Hands on working knowledge of the DS/ML stack (NumPy, Pandas, SciPy, Scikit learn, TensorFlow, PyTorch).

- Working experience in SQL.

- Hands-on experience with cloud-based data handling and ML workflows (Google Cloud preferred).

- Experience in building microservice APIs for ML solutions.

- Exposure to model monitoring technologies.

- Experience with deploying ML solutions at scale (Kubeflow, VertexAI, Airflow, PySpark).

- Proficiency with data visualization libraries (matplotlib, seaborn, plotly, etc.)

- Excellent communication and collaboration skills for working across global teams.

Responsibilities :

- Design, develop, and deploy scalable machine learning models using Python and modern ML frameworks

- Translate business and functional requirements into data-driven solutions and ML use cases

- Build, evaluate, and optimize ML models using NumPy, Pandas, SciPy, Scikit-learn, TensorFlow, and PyTorch

- Perform data exploration, feature engineering, and statistical analysis to support model development

- Develop and deploy ML solutions using cloud-based platforms with preference for Google Cloud

- Build and maintain microservice-based APIs to serve ML models in production

- Implement and manage end-to-end ML workflows including training, validation, deployment, and monitoring

- Deploy and scale ML pipelines using tools such as Kubeflow, Vertex AI, Airflow, and PySpark

- Monitor model performance, data drift, and system health in production environments

- Collaborate with cross-functional and global teams to deliver high-quality data science solutions

- Create clear data visualizations and insights using libraries such as Matplotlib, Seaborn, and Plotly

- Write clean, efficient, and production-quality code and participate in code reviews

- Support UAT and production rollouts of ML solutions

- Troubleshoot, debug, and resolve complex issues in ML models and data pipelines

- Conduct proof-of-concepts to validate model approaches, tools, and technologies

- Continuously improve model accuracy, performance, and reliability through experimentation

- Provide technical guidance and constructive feedback to team members