Sr DATA SCIENTIST - Python

• Strong hands-on experience with Python 3.x
• Strong programming and problem-solving skills
• Ability to write Python programs from scratch without relying heavily on built-in functions
• Good understanding of time and space complexity
• Experience with FastAPI / Flask and development of REST APIs
• Strong knowledge of Pandas, NumPy and SQLAlchemy
• Experience with scikit-learn and classical machine learning techniques
• Strong understanding of:
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • XGBoost
  • LightGBM
  • CatBoost
• Hands-on experience with classification and regression problems
• Strong understanding of ML evaluation metrics including:
  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • ROC-AUC
  • MAE
  • MSE
  • RMSE
  • R

• Hands-on experience in forecasting and time-series machine learning
• Understanding of:
  • Time-series data preparation
  • Feature engineering
  • Lag features
  • Rolling statistics
  • Train/test split for time-series data
  • Forecasting evaluation
• Experience with classical forecasting/ML techniques such as:
  • Linear Regression
  • Random Forest
  • XGBoost
  • ARIMA/SARIMA or similar forecasting approaches
• Ability to select appropriate forecasting algorithms based on business requirements and data characteristics

• Understanding of the end-to-end machine learning lifecycle
• Model development, training, validation and deployment
• Model performance monitoring
• Data drift and model drift monitoring
• Data quality and validation frameworks
• Handling:
  • Missing data
  • Outliers
  • Feature drift
  • Class imbalance
  • Data leakage
• Experience with model retraining and performance monitoring strategies
• Understanding of A/B testing and experimentation

• Experience designing and implementing ML solutions for real-world business problems
• Understanding of ensemble learning and gradient boosting
• Ability to explain algorithm selection and trade-offs
• Experience integrating business/engineering rules with ML predictions
• Understanding of probabilistic vs. deterministic decision frameworks
• Experience handling complex ML problems such as:
  • Class imbalance
  • Correlated features
  • Spatial/temporal dependencies
  • Model interpretability
• Experience designing scalable ML/analytics pipelines

• Experience developing Python-based microservices and REST APIs
• Experience with FastAPI/Flask
• Docker/containerization
• Git
• SQL/database integration
• Experience deploying Python/ML solutions in cloud environments is preferred

• Lists
• Dictionaries
• Sets
• Strings
• Sorting/searching
• Array manipulation
• Data transformation
• Merging datasets/lists
• Basic algorithms and data structures
• Optimization of solutions
• Time and space complexity analysis
• Ability to explain multiple approaches to the same problem

1. Python
2. Classical ML
3. Forecasting
4. Regression & Classification
5. XGBoost/LightGBM/CatBoost
6. ML lifecycle
7. Model evaluation
8. Model monitoring
9. Python coding/DSA
10. Time & Space Complexity

• FastAPI/Flask
• Docker
• SQLAlchemy
• REST APIs
• Data quality
• Data drift
• Cloud

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