Data Science

• Programming Languages:

  Python (must-have), SQL (proficiency).

• Machine Learning Libraries:

  scikit-learn, XGBoost, LightGBM, TensorFlow or PyTorch (basic knowledge).

• Data Visualization:

  Power BI, Tableau, matplotlib, seaborn, Plotly.

• Data Manipulation:

  Pandas, NumPy, SQL, Regex, NLP libraries

• Statistical Techniques:

  Regression, Classification, Clustering, Time Series Forecasting, A/B Testing.

• Cloud Platforms:

  AWS / GCP / Azure (experience with at least one).

• Model Deployment:

  Experience with Flask, FastAPI, Docker, or MLflow is a plus.

• Version Control:

  Git/GitHub

Good To have:

• Advanced Machine Learning
• Elastic Search, Open Search

Database & Performance

• Database Design, Connection Pooling, Caching Strategies, Error Handling
• Postgresql, MongoDB
• Redis, Kafka - Caching strategies and async operations

Soft Communication

• Technical Communication - Ability to explain complex technical concepts
• Problem-Solving - Debugging complex distributed systems
• Strong bias for action & driving results in a high-performance environment.

Exceptionally high motivational levels and needs to be a self-starter.