Data Scientist II

Job description

## **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**. - 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 - **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**. *We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status or any other category prohibited by applicable law.*