Predictive Analytics / Machine Learning (ML) Engineer

Key Responsibilities: Model Development & Deployment Design, build, and deploy predictive and machine learning models for forecasting, classification, recommendation, and optimization tasks. Select and implement appropriate algorithms (e.g., regression, decision trees, random forests, neural networks) based on business problems. Develop pipelines to integrate models into production systems for real-time or batch predictions. Data Preparation & Feature Engineering Collect, clean, and preprocess structured and unstructured data from multiple sources. Perform feature engineering, selection, and dimensionality reduction to improve model accuracy. Collaborate with data engineers to ensure availability of high-quality datasets. Model Evaluation & Optimization Evaluate models using metrics such as accuracy, precision, recall, F1-score, ROC-AUC, and mean squared error. Tune hyperparameters, optimize performance, and prevent overfitting or bias in models. Monitor deployed models and update/retrain as necessary to maintain predictive accuracy. Collaboration & Documentation Work with business stakeholders to translate business requirements into analytical solutions. Document model designs, assumptions, limitations, and results. Collaborate with software engineers, DevOps teams, and data scientists for integration and deployment. Research & Continuous Improvement Stay current with emerging machine learning frameworks, algorithms, and tools. Evaluate new methodologies to improve model accuracy, scalability, and interpretability. Promote best practices for ML model governance, reproducibility, and performance monitoring.

Qualifications: Required2–5+ years of experience in machine learning, predictive analytics, or data science roles. Strong programming skills inPython, R, or Java, with experience in ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).Solid understanding of statistics, data modeling, and predictive analytics techniques. Experience in data preprocessing, feature engineering, and model evaluation. Familiarity with deploying models to production environments and integrating with applications. Preferred Experience with big data frameworks (e.g., Spark, Hadoop) and cloud ML services (AWS SageMaker, Azure ML, GCP AI Platform).Knowledge of deep learning techniques, NLP, or time-series forecasting. Experience with containerization (Docker, Kubernetes) and CI/CD for ML pipelines (MLOps).Understanding of data governance, privacy, and compliance standards.