Data Scientist II

Job Title: Data Scientist II Company: CommerceIQ Job Category: Machine Learning Roles & Responsibilities: * 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. 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. Work Location: Bengaluru, Karnataka, India Availability: Full time

* 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. 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. Key Skills: Machine Learning, Python, Pandas, NumPy, Feature Engineering, NLP Salary: $200