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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - AI/ML & Predictive Analytics - **Company:** Wintrio Llc - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Computer Vision, Microsoft Azure, Cloud Computing, Cluster Analysis, Program Optimization, Computer Programming, Databases, Data Cleansing, Data Transformation, Data Visualization, Decision Support Systems, Python (Programming Language), Machine Learning, Natural Language Processing, NoSQL, Performance Tuning, Recommender Systems, Standard Sql, Unstructured Data, Google Cloud, Feature Engineering, Flask (Web Framework), Large Language Models, Model Validation, Generative AI, Git, Fastapi, Matplotlib, AI Platforms, Information Technology, Dask, Plotly, Feature Selection, Machine Learning Operations, Restful APIs, Software Version Control, Serverless Computing, Docker - **Published:** July 8, 2026 - **Apply:** https://www.wintrio.com/careers/data-scientist-ai-ml-predictive-analytics/ ## About the Role The ideal candidate possesses strong expertise in statistics, predictive analytics, machine learning, and programming, with experience deploying production-ready AI/ML solutions within enterprise or cloud environments., * Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Artificial Intelligence, Engineering, or a related field. * Minimum five (5) years of experience in Data Science, Machine Learning, Predictive Analytics, or Artificial Intelligence. * Strong programming experience using Python or R. * Strong understanding of statistics, probability, predictive modeling, and machine learning algorithms. * Experience working with large structured, semi-structured, and unstructured datasets. * Experience developing and deploying machine learning models into production environments. * Strong written and verbal communication skills. * Strong analytical, mathematical, and problem-solving abilities., * Python * R Python Libraries * NumPy * Pandas * Scikit-learn Machine Learning Frameworks * TensorFlow * PyTorch * XGBoost * LightGBM Data Processing Platforms * Apache Spark * Databricks * Dask Data Visualization * Matplotlib * Plotly * Seaborn Model Deployment * REST APIs * Flask * FastAPI * Docker Cloud AI Platforms * AWS SageMaker * Microsoft Azure Machine Learning * Google Cloud AI Platform Databases * SQL * NoSQL Databases Version Control & MLOps * Git * MLflow * Data Version Control (DVC) Machine Learning Concepts * Cross-Validation * Hyperparameter Tuning * Feature Engineering * SHAP, * Experience supporting Federal analytics, artificial intelligence, or machine learning programs. * Experience deploying production-ready machine learning models within cloud environments. * Experience supporting MLOps, model governance, and AI lifecycle management. * Experience with Natural Language Processing (NLP), Large Language Models (LLMs), Generative AI, or Computer Vision. * Experience working with sensitive, regulated, or high-volume datasets. * Familiarity with Federal AI governance, responsible AI practices, and emerging AI technologies. ## Description * Design, develop, train, validate, and deploy machine learning models supporting classification, regression, clustering, forecasting, recommendation, and anomaly detection. * Perform exploratory data analysis (EDA), feature engineering, feature selection, and data preparation for machine learning applications. * Develop predictive analytics models supporting forecasting, trend analysis, operational optimization, and decision support. * Evaluate, tune, and optimize machine learning models using cross-validation, hyperparameter optimization, and performance evaluation techniques. * Deploy machine learning models into production environments using APIs, cloud-native services, containers, or automated ML pipelines. * Work with structured, semi-structured, and unstructured datasets across enterprise and cloud platforms. * Collaborate with Data Engineers to integrate AI/ML models into enterprise data pipelines and production systems. * Document models, assumptions, methodologies, validation results, and technical findings for both technical and business stakeholders. * Support AI governance initiatives, including model explainability, fairness, reproducibility, monitoring, and lifecycle management. * Develop dashboards, reports, and visualizations that communicate analytical insights to executive leadership and program stakeholders. * Support continuous improvement initiatives focused on AI innovation, predictive analytics, and data modernization., * Predictive Analytics * Classification Models * Regression Models * Clustering * Forecasting ## Related Videos - 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