Senior Data Scientist / AI Scientist

BluJuniper is a rapidly growing, SBA certified woman owned management consulting and IT services firm focused on modernizing government and commercial enterprises through integrated governance, digital transformation, and data driven performance. We specialize in architecting enterprise solutions that deliver measurable results across technology, strategy, and operations. This role is ideal for a senior data scientist or AI scientist who thrives in developing advanced analytical models, machine learning systems, data mining workflows, and data visualization solutions for complex technical environments. You will support the design, development, evaluation, and deployment of AI and data science capabilities across government and commercial customers. You will work closely with cross functional engineering, operations, and customer facing teams to transform complex data into actionable insights, reliable models, clear visualizations, and production ready AI capabilities. Key Responsibilities Data Science and AI Development Design, develop, test, and evaluate machine learning and deep learning models. Build analytical workflows using Python, PyTorch, Pandas, NumPy, and related tools. Develop data pipelines for cleaning, transforming, analyzing, and validating large datasets. Apply statistical analysis, feature engineering, model evaluation, experimentation, and optimization techniques. Translate ambiguous technical and business problems into structured analytical approaches. Data Mining and Exploratory Analysis Identify patterns, trends, anomalies, relationships, and insights within large and complex datasets. Perform exploratory data analysis to assess data quality, uncover useful signals, and guide modeling approaches. Apply data mining techniques to structured, semi structured, and unstructured data. Develop repeatable methods for extracting, organizing, and interpreting information from diverse data sources. Communicate findings clearly to technical and non technical stakeholders. Machine Learning and Deep Learning Develop and evaluate supervised, unsupervised, and deep learning models. Work with neural networks, model architectures, training workflows, optimization methods, and evaluation metrics. Perform model tuning, validation, error analysis, and performance benchmarking. Support integration of AI models into larger software systems and operational workflows. Stay current with emerging AI, machine learning, and data science methods. Data Visualization and Communication Create clear, useful, and visually compelling data visualizations, dashboards, reports, and analytical summaries. Use visualization tools and libraries to communicate trends, uncertainty, model performance, and key findings. Present complex analytical results in a way that is understandable to technical teams, leadership, and customers. Support decision making by turning data outputs into interpretable insights. Maintain documentation for datasets, assumptions, methods, experiments, and results. Data Engineering and Model Deployment Support Collaborate with software engineers and DevOps teams to support model deployment and production integration. Work with structured, semi structured, and unstructured data from a variety of sources. Support reproducible research and development workflows using version control, experiment tracking, and documentation. Help design scalable data processing and model training workflows. Support cloud, hybrid, and on premises deployment models as needed. Quality, Security, and Documentation Write clean, maintainable, and well documented Python code. Follow responsible AI, secure development, and data governance practices. Create and maintain documentation for models, datasets, assumptions, experiments, evaluation results, and visualizations. Participate in technical reviews, model reviews, and continuous improvement efforts. Communicate technical findings clearly to both technical and non technical stakeholders. Required Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Physics, or a related technical field. 5+ years of professional experience in data science, machine learning, artificial intelligence, applied analytics, or a related field. Strong proficiency in Python. Hands on experience with PyTorch. Strong experience with Pandas, NumPy, Scikit-learn, and common data science libraries. Experience developing, training, evaluating, and improving machine learning or deep learning models. Experience with data mining, exploratory data analysis, feature engineering, and extracting insights from complex datasets. Experience creating data visualizations, dashboards, reports, or analytical summaries. Strong understanding of statistics, probability, optimization, and model evaluation methods. Experience working with large, messy, real world datasets. Ability to write clean, maintainable code and work in version controlled environments. Strong written and verbal communication skills. Preferred PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, Physics, or a related technical field. Experience supporting defense, national security, federal government, or mission critical programs. Experience with deep learning architectures, computer vision, natural language processing, time series modeling, reinforcement learning, or anomaly detection. Experience with MLOps practices, model deployment, experiment tracking, and production model monitoring. Experience with cloud platforms, containerized workflows, Docker, Kubernetes, or distributed computing. Experience with visualization tools and libraries such as Matplotlib, Plotly, Dash, Tableau, Power BI, or similar tools. Familiarity with data governance, responsible AI, model explainability, and security requirements. Experience working with geospatial data, sensor data, operational data, or other complex technical datasets. Active Security Clearance or the ability to obtain one. #J-18808-Ljbffr