Data Scientist II

The Company CommerceIQ is building the AI platform that runs commerce for the world's largest brands. We are not selling AI demos. We are shipping AI agents for content, media, and sales into the workflows of the Fortune 100 every week. 2,200+ Customers 10 of Top 12 CPG Companies 900+ Retailers Connected $200M+ Raised Customers include Coca-Cola, Nestlé, Colgate-Palmolive, Mondelez, Samsung, and Kellogg's. Backed by SoftBank, Insight Partners, and Madrona. Headquartered in Mountain View with teams across the US, India, Canada, and the UK. Pre-IPO. 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.