{"id":4261301,"url":"https://alion.io/job/maes-solutions-senior-data-scientist","title":"Senior Data Scientist","company":{"id":4434886,"name":"MAES Solutions","domain":"maessolutions.com","url":"https://alion.io/company/maes-solutions","size_band":"51-200","is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Azure","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"Keras","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Matplotlib","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Power BI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Seaborn","optional":false},{"name":"Sentiment Analysis","optional":false},{"name":"Spark","optional":false},{"name":"Tableau","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"Tokenization","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-11-13T14:31:33Z","employer_posted_date":null,"last_verified_at":"2026-11-13T14:31:33Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"MAES Solutions is seeking a Data Scientist to develop and implement machine learning, statistical modeling, predictive analytics, and AI solutions. The role focuses on building and deploying models, developing end-to-end data science workflows, and communicating insights to business stakeholders.\n\nResponsibilities\nDevelop, train, validate, and deploy machine learning models for classification, regression, clustering, forecasting, recommendation, anomaly detection, and predictive analytics use cases\nPerform exploratory data analysis (EDA) , statistical analysis, data profiling, data preprocessing, data cleansing, and feature engineering to identify meaningful patterns and business insights\nBuild predictive models using Python, Scikit-learn, Pandas, NumPy, XGBoost, LightGBM, and other machine learning frameworks\nApply advanced statistical modeling, hypothesis testing, regression analysis, probability, time-series analysis, correlation analysis, and experimental design to solve complex business problems\nDevelop and optimize Deep Learning models using frameworks such as T ensorFlow, PyTorch, Keras, and related AI/ML technologies \nDesign and implement Natural Language Processing (NLP) solutions for text classification, sentiment analysis, document processing, information extraction, text summarization, and semantic analysis\nWork with Generative AI, Large Language Models (LLMs), embeddings, vector databases, prompt engineering, Retrieval-Augmented Generation (RAG), and AI-powered applications where applicable\nDevelop end-to-end data science solutions, from data collection and preprocessing through model development, evaluation, deployment, monitoring, and continuous improvement\nEvaluate model performance using appropriate evaluation metrics, cross-validation, hyperparameter tuning, model selection, explainability, and statistical validation techniques\nImplement MLOps practices model versioning, experiment tracking, deployment automation, model monitoring, drift detection, and lifecycle management\nWork with cloud-based AI/ML platforms such as Microsoft Azure, Azure Machine Learning, Azure Databricks, Azure Data Lake, and Azure Synapse Analytics\nDevelop scalable data processing and machine learning workflows using Apache Spark, PySpark, Databricks, and distributed computing technologies\nCreate dashboards, visualizations, and analytical reports using Power BI, Tableau, Matplotlib, Seaborn, or other visualization tools to communicate insights to business stakeholders\n\nSkills\n8+ years of experience in Data Science, Machine Learning, Predictive Analytics, Statistical Modeling, and Advanced Analytics\nStrong programming expertise in Python, with hands-on experience using Pandas, NumPy, Scikit-learn, SciPy, Matplotlib , and Seaborn\nExtensive knowledge of Machine Learning algorithms , including Linear/Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, SVM, K-Means, Clustering, PCA, and Ensemble Methods\nHands-on experience with Deep Learning frameworks such as TensorFlow, PyTorch, and Keras, including neural networks and model optimization\nExperience with Natural Language Processing (NLP), including text classification, sentiment analysis, tokenization, embeddings, transformers, and language models\nStrong understanding of Generative AI, Large Language Models (LLMs), Prompt Engineering, RAG, Vector Databases, Embeddings, and AI application development\n\nCompany Overview\nMAES Solutions is an IT consulting company offering infrastructure management, cybersecurity, and software development services. It was founded in 2020, and is headquartered in East Rutherford, New Jersey, USA, with a workforce of 11-50 employees. Its website is https://www.maessolutions.com.\n\nCompany H1B Sponsorship\nMAES Solutions has a track record of offering H1B sponsorships, with 6 in 2026, 2 in 2025, 12 in 2024, 1 in 2023. 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