ABS Group is seeking a Data Scientist to join our Artificial Intelligence Practice in support of a major federal modernization program. In this role, you will apply machine learning, statistical analysis, and modern AI techniques to large, complex datasets that support mission-critical planning and operations. You will work alongside data scientists, engineers, and client stakeholders to build analytical solutions that improve data quality, support decision-making, and help move advanced analytics from prototype to operational use. The scope of responsibility, degree of technical leadership, and level of independence expected will reflect the level at which this position is filled. What You Will Do: • • Analyze large and complex datasets using statistical methods, machine learning, and AI techniques • • Build, test, and maintain predictive models and analytical workflows • • Apply methods such as machine learning, deep learning, NLP, generative AI, large language models, and time series analysis to mission-focused data problems • • Support feature engineering, data preparation, model evaluation, and reproducible analytical development • • Help transition analytics and machine learning capabilities from pilot efforts into production environments • • Contribute to synthetic data development and other advanced analytical methods where appropriate • • Partner with engineers and stakeholders to translate business needs into practical analytical solutions • • Prepare technical documentation, reports, presentations, and other project deliverables • • Support responsible AI practices, including explainability, trustworthiness, and AI risk management • What You Will Need: Level and Compensation: This position may be filled at multiple career levels based on business need and the selected candidate's qualifications, relevant experience, and demonstrated capability. Typical leveling for this role is as follows: • Junior (0 to 4 years) • Mid-Level (5 to 9 years) • Senior (10 to 14 years) • Principal (15 or more years) These ranges are guidelines only and are not the sole determining factor in level. The posted compensation range reflects the full range across all possible levels. Individual offers will be based on the level at which the candidate is hired, as well as factors such as skills, experience, internal equity, and geographic market considerations where applicable. Education and Experience • • Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related STEM field; Master's degree preferred • • Relevant professional experience applying data science, machine learning, or advanced analytics; the depth and scope of experience required will vary based on the level at which this position is filled • • Experience working with large, real-world datasets across the full analytical lifecycle preferred • • Experience supporting production or operational deployment of analytical or machine learning solutions a plus • Knowledge, Skills, and Abilities • • Proficiency in Python and working knowledge of SQL • • Experience with machine learning, predictive modeling, model evaluation, and feature engineering • • Familiarity with NLP, GenAI, and LLM methods • • Familiarity with deep learning, neural networks, anomaly detection, and time series methods • • Strong communication skills, including the ability to explain technical findings clearly to varied audiences • • Ability to develop reproducible analytical workflows and clear technical documentation • Nice to Have: • • Experience in a federal, public sector, or regulated data environment • • Familiarity with survey, demographic, statistical, or population-scale data • • Exposure to record linkage, entity resolution, or matching models • • Experience with synthetic data, differential privacy, or responsible AI practices • Reporting Relationships: Reports directly to the Chief Data Scientist or a Manager, Director, or Executive role. Location This position is based in the Washington, D.C. metro area, with primary work performed in Suitland, Maryland. Remote or hybrid arrangements may be available for eligible work, subject to government approval.