Data Scientist 2

Technical Expertise

• Strong background in machine learning, statistical modeling, predictive analytics, and feature engineering, with hands-on experience developing and deploying classical ML models.
• Strong proficiency in classical machine learning algorithms including Linear Regression, Logistic Regression, Decision Trees, Random Forest, XGBoost, LightGBM, CatBoost, Support Vector Machines (SVM), Naive Bayes, K-Means, and DBSCAN.
• Experience with feature engineering, feature selection, dimensionality reduction, hyperparameter optimization, cross-validation, model calibration, and model evaluation.
• Proficiency in Python, Pandas, NumPy, Scikit-learn, with experience using libraries such as XGBoost, LightGBM, CatBoost, and Statsmodels.
• Experience working with large datasets and data pipelines, including data preprocessing, data quality checks, transformation, aggregation, and feature generation using tools such as SQL, Spark/PySpark, and cloud data platforms.
• Strong understanding of statistical methods and model diagnostics, including hypothesis testing, confidence intervals, correlation analysis, distribution analysis, and statistical significance.
• Experience with model deployment, monitoring, retraining, and productionization of classical machine learning models.

Applied Problem-Solving

• Mandatory skill — Demonstrated experience building and deploying classical machine learning models for real-world business problems such as customer churn, credit risk, fraud detection, demand forecasting, customer segmentation, recommendation systems, pricing, propensity modeling, or sales prediction.
• Mandatory skill — Strong ability to design, evaluate, and improve ML models using robust validation strategies, cross-validation, hyperparameter tuning, feature engineering, and model comparison.
• Experience selecting appropriate algorithms based on business objectives, data characteristics, interpretability requirements, and model performance.
• Strong understanding of model evaluation metrics such as ROC-AUC, PR-AUC, Precision, Recall, F1, Log Loss, RMSE, MAE, MAPE/WMAPE, Gini, KS, R², and other domain-specific metrics.
• Experience with model interpretability and explainability, using techniques such as SHAP, Partial Dependence Plots (PDP), feature importance, permutation importance, and coefficient analysis.
• Experience identifying and addressing data quality issues, class imbalance, overfitting, multicollinearity, feature leakage, model bias, distribution shift, and model drift.
• Experience applying statistical and machine learning techniques to NLP, time-series forecasting, classification, regression, clustering, recommendation, or optimization problems.

Leadership & Collaboration

• Preferred: Proven ability to mentor junior data scientists or analysts, provide technical guidance, and establish best practices for machine learning development.
• Strong cross-functional collaboration skills with product, engineering, business, and analytics stakeholders to translate business problems into measurable ML solutions.
• Ability to communicate model assumptions, methodology, results, limitations, and business impact to both technical and non-technical stakeholders.
• Ability to translate analytical findings into practical, scalable, and measurable business solutions.

Education & Experience

• 3+ years of hands-on experience in classical machine learning, data science, predictive modeling, or statistical modeling.
• Master's or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, Engineering, or a related field, or equivalent practical experience.
• Strong analytical and problem-solving skills with the ability to work with structured and unstructured datasets.
• Excellent communication and presentation skills, with the ability to explain complex analytical and statistical concepts clearly.
• Strong understanding of machine learning fundamentals, statistics, probability, and optimization.
• Continuous learner with awareness of classical machine learning techniques, statistical modeling methodologies, model interpretability, and emerging best practices in applied data science.