Data Scientist I

About this role

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Skills you'll use

What you'll do

• 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 .

Who can apply

• 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.

About CommerceIQ

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