Job Title: Machine Learning Engineer / Data Scientist (Predictive Modeling – Classification, Regression, Clustering) Location: Jersey city, NJ (Hybrid/Remote) Job Type: Contract/ Full-Time • (Classification/Regression) and unsupervised/clustering ML models looking for hands-on experience around these predictive models (from data pre-processing to EDA to Modeling to Evaluation). • Classification – Logistic Regression, Decision Tree, Random Forest • Regression – Linear, Gradient Boosting, Neural Nets, KNN • Unsupervised – K-means and other clustering technique Job Summary: We are seeking a skilled and hands-on Machine Learning Engineer/Data Scientist to design, develop, and evaluate predictive models for classification, regression, and clustering use cases. The ideal candidate will have strong experience across the full ML pipeline—from data preprocessing and EDA to model building and performance evaluation. Key Responsibilities: • Perform data preprocessing including cleaning, feature engineering, encoding, scaling, and handling missing values. • Conduct detailed exploratory data analysis (EDA) using statistical methods and data visualization tools. • Build, tune, and evaluate classification models like Logistic Regression, Decision Trees, Random Forests. • Develop regression models using Linear Regression, KNN, Gradient Boosting, and Neural Networks. • Implement and evaluate unsupervised learning techniques such as K-means, DBSCAN, and Hierarchical Clustering. • Use dimensionality reduction techniques like PCA and t-SNE for visualization and preprocessing. • Measure model performance using appropriate metrics (Accuracy, F1-score, RMSE, Silhouette Score, etc.). • Work with large datasets using libraries like pandas, numpy, scikit-learn, xgboost, keras, and matplotlib/seaborn. • Collaborate with data engineers and analysts to deploy and maintain models in production environments. • Stay updated with latest ML research and best practices. Required Skills & Qualifications: • Bachelor’s or Master’s degree in Computer Science, Statistics, Data Science, or related field. • 2–5 years of experience in machine learning or data science roles. • Strong hands-on experience with: • Classification: Logistic Regression, Random Forest, Decision Trees • Regression: Linear Regression, Gradient Boosting, Neural Nets, KNN • Unsupervised Learning: K-Means, Clustering Techniques • Proficient in Python and core ML libraries (scikit-learn, xgboost, keras, pandas, numpy). • Experience with EDA and data visualization (matplotlib, seaborn, plotly). • Good understanding of model evaluation metrics and cross-validation. • Experience with Jupyter Notebooks, Git, and version control. • Excellent problem-solving skills and attention to detail. Preferred Qualifications: • Exposure to cloud platforms (AWS, GCP, or Azure) • Experience with MLOps or model deployment • Experience with SQL and data extraction from databases