Strong proficiency in Python, including NumPy, pandas, and scikit-learn, with basic knowledge of PyTorch or TensorFlow
Solid experience in exploratory data analysis (EDA) and feature engineering
Strong foundation in statistics and probability, including hypothesis testing, inference, and distributions
Experience building, evaluating, and tuning supervised and unsupervised machine learning models
Proficiency in SQL for data analysis and querying
Experience with ML experimentation and tracking tools such as MLflow, Weights & Biases, or Databricks ML
Understanding of model evaluation and validation strategies, including cross-validation, metrics, and overfitting
Basic knowledge of cloud-based ML platforms such as Azure ML, AWS SageMaker, or GCP Vertex AI
Experience with data visualization libraries (Matplotlib, Seaborn, Plotly) and BI tools (Power BI or Tableau)
Understanding of MLOps fundamentals, including model versioning, registries, and deployment lifecycle
Explore, clean, and prepare data for analysis and modeling
Design, build, and evaluate statistical and machine learning models
Run structured experiments and validate results using sound scientific methods
Document methodologies, assumptions, metrics, and key decisions
Communicate insights and results clearly to both technical and business audiences
Collaborate closely with Data Scientists, Data Engineers, ML Engineers, Product Owners, and Subject Matter Experts
Support MLOps handover by providing deployment artifacts and model documentation
Monitor model performance and data drift, contributing to retraining and improvement plans
Ensure alignment between analytical solutions and business objectives
