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.

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.

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.

1+ 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.

Data Scientist II

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