Data Scientist - AI/ML & Advanced Analytics (Fraud Analytics & Investigative Support)
About The Position
Praescient Analytics is building a multidisciplinary advanced analytics team supporting federal fraud detection and investigative missions. We are seeking experienced Data Scientists with expertise in one or more advanced analytical disciplines, including artificial intelligence (AI), machine learning (ML), natural language processing (NLP), large language models (LLMs), graph analytics, and relationship discovery. These positions will help design and implement next-generation analytical capabilities that identify hidden fraud patterns, uncover complex relationships, analyze unstructured information, and transform large, diverse datasets into actionable intelligence for investigators and oversight organizations. The ideal candidate is a hands-on technical specialist who enjoys applying emerging analytical technologies to solve complex fraud, financial crime, and investigative challenges.
Requirements
- Must have experience with Fraud Analysis
- Three (3) or more years of professional experience developing advanced analytical or machine learning solutions.
- Strong Python and SQL programming experience.
- Experience developing, testing, validating, and improving analytical or machine learning models.
- Experience working with cloud analytics environments.
- Excellent analytical, written, and verbal communication skills.
- US Citizenship Required
Nice To Haves
- Artificial Intelligence & Machine Learning: Developing, validating, deploying, and optimizing machine learning and AI models using modern frameworks and best practices for predictive analytics, classification, clustering, and model evaluation.
- Natural Language Processing (NLP) & Large Language Models (LLMs): Applying NLP, LLMs, Retrieval-Augmented Generation (RAG), semantic search, information extraction, document intelligence, and other techniques to analyze and derive insights from unstructured text.
- Graph Analytics & Relationship Discovery: Leveraging graph databases, knowledge graphs, link analysis, network analytics, entity resolution, and relationship discovery tools (e.g., Neo4j, Cypher, i2 Analyst's Notebook) to identify hidden patterns and complex fraud networks.
- Cloud-Native Analytics: Developing analytical solutions within modern cloud and Lakehouse environments using platforms such as Azure Databricks, Microsoft Fabric, Azure Data Lake Storage, SQL Server, Power BI, Git, or comparable technologies.
- Fraud Analytics & Investigative Support: Applying advanced analytics to fraud detection, financial crimes, program integrity, federal benefit programs, grants, loans, emergency relief, or other government oversight and investigative missions.
Responsibilities
- Design, develop, validate, and optimize advanced analytical models supporting fraud detection and investigative missions.
- Apply machine learning, artificial intelligence, natural language processing, graph analytics, and statistical modeling techniques to identify fraud patterns and emerging risks.
- Analyze structured, semi-structured, and unstructured data from multiple government and commercial sources.
- Develop scalable analytical workflows using cloud-native technologies and open-source data science frameworks.
- Collaborate with Graph Data Scientists, Data Engineers, Investigative Analysts, and Technical Analytics Managers to develop integrated analytical solutions.
- Document analytical methodologies, model performance, validation results, and technical recommendations.
- Support Agile software development through sprint planning, demonstrations, peer reviews, and iterative solution development.
Benefits
- Competitive salary based on qualifications and experience
- Comprehensive, Company paid healthcare for you (We pay your premiums and deductibles)
- 401(k) with company match
- Travel & performance incentives
- 3 weeks paid time off (plus Federal Holidays)
- $5K annual training allowance
- $500 book allowance
- Tuition reimbursement program
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
