United States onlyQualifications:
- A degree in Computer Science, Information Systems, Engineering, Business Administration, or other related discipline is preferred.
- 3+ years of professional experience developing and implementing AI models and algorithms to address complex business and mission challenges.
- Programming proficiency (e.g., Python, R, SQL, Java, Scala, C++, Tableau).
- Experience and understanding of machine learning (ML) and related algorithms (e.g., linear regression, logistic regression, decision trees, random forests, support vector machines, and gradient boosting methods), unsupervised learning (e.g. clustering algorithms (K-Means, hierarchical clustering, DBSCAN), dimensionality reduction techniques (e.g., PCA, t-SNE), and anomaly detection methods, and familiarity with reinforcement learning principles.
- Understand the architecture and training of different types of neural networks (e.g., N0017825R3013 Page 5 of 8 feedforward networks, convolutional neural networks, recurrent neural networks, transformers).
- Experience building, training, and deploying deep learning models, familiarity with Natural Language Processing techniques such as but not limited to text classification, sentiment analysis, named entity recognition, and topic modeling. Knowledge of object detection, image segmentation, and image classification.
- Knowledge of Data Wrangling and Preprocessing, Statistical Analysis, Model Evaluation and Selection, Model Deployment and Monitoring, Big Data Technologies, and Data Visualization (e.g. Tableau, Power BI, Matplotlib, Seaborn).
- Collaborate with data engineers to preprocess and clean data for AI models. Create and maintain documentation for AI models, algorithms, and workflows.
- Troubleshooting and resolving issues related to AI systems.
- Report findings and recommendations to stakeholders, including technical and non-technical audiences.
- Conduct research, design, and implement AI/ML models to support program objectives as outlined in the PWS.
- Develop, test, and validate machine learning algorithms to analyze structured and unstructured data sets.
- Collaborate with program managers, engineers, and analysts to identify opportunities for AI/ML integration into operations.
- Perform data preprocessing, cleaning, feature engineering, and exploratory data analysis (EDA).
- Utilize natural language processing (NLP), computer vision, or predictive analytics tools as required by project tasks.
- Generate dashboards, visualizations, and reports to communicate findings and support decision-making.
- Support the automation of repetitive tasks and workflows through AI/ML-driven solutions.
- Evaluate the performance, accuracy, and reliability of AI/ML models against mission-specific requirements.
- Ensure compliance with data governance, privacy, and security standards while handling sensitive data.
- Stay current with emerging AI/ML technologies and provide recommendations for adoption and improvement.
- Provide technical documentation, model explainability, and briefing materials for stakeholders and leadership.
- Assist in training and knowledge transfer sessions to enable staff to effectively utilize AI/ML solutions.
