Must Have skills
Python
Dash
Databricks
FastAPI
GitHub Actions
Matplotlib
NumPy
Pandas
PySpark
PyTorch
Scikit-learn
SciPy
SQL
Streamlit
TensorFlow
XGBoost
Develop and deploy predictive and prescriptive models to solve business problems using statistical and machine learning techniques.
Analyze large and complex datasets to extract actionable insights and support data-driven decision-making.
Design, train, validate, and optimize machine learning models for classification, regression, clustering, and forecasting tasks.
Build and maintain scalable ML pipelines for model training, evaluation, and deployment in production settings.
Collaborate with cross-functional teams to define use cases, gather data requirements, and interpret model outputs.
Implement model monitoring,



versioning, and retraining strategies to ensure performance and reliability over time.
Use Python extensively for data manipulation, model development, automation, and integration with APIs and data platforms.
Apply MLOps practices to streamline model lifecycle management, including CI/CD for ML workflows.
Leverage cloud-based ML services (e.g., Azure ML, AWS SageMaker, GCP Vertex AI) for scalable experimentation and deployment.
Document methodologies, model assumptions, and results for transparency, reproducibility, and stakeholder communication.

📌 Data Scientist 30th July Thursday Virtual Interview Bengaluru (India)
🏢 Tata Consultancy Services
📍 India