AI Data Scientist

Location: Remote

Responsibilities

• Partner with stakeholders to define and deliver AI/analytics use cases, translating business needs into scalable data science solutions.
• Design and develop machine learning models and analytical approaches to support search, discovery, and insight generation across structured and unstructured data.
• Build and implement NLP, semantic search, and entity resolution capabilities to enable advanced information retrieval and relationship analysis.
• Leverage document‑based data (e.g., OCR/ICR outputs, metadata, and free text) to extract insights and support downstream analytics and search solutions.
• Collaborate with data engineers to integrate models into production environments, including Palantir Foundry, Databricks, and AWS‑based platforms.
• Develop model evaluation frameworks, confidence scoring, and explainability approaches to ensure transparency and usability of AI outputs.
• Support development of analytics, reporting, and dashboards to drive operational insights and decision‑making.
• Operate within an Agile delivery model, contributing to sprint planning, experimentation, and iterative solution delivery.
• Communicate findings and recommendations clearly to both technical and non‑technical audiences, including client stakeholders.
• Contribute to solution design, proposal support, and thought leadership in AI/analytics capabilities.

Requirements

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Minimum of 4 years of experience in data science, machine learning, or applied analytics roles.
• U.S. citizenship required and ability to obtain and maintain a Public Trust clearance.
• Experience developing and applying machine learning models, including Natural Language Processing (NLP), semantic search or information retrieval, and entity resolution or relationship modeling.
• Experience working with large‑scale structured and unstructured data, particularly document‑based datasets (e.g., text, PDFs, images).
• Experience leveraging metadata and extracted features to support analytics and modeling.
• Strong proficiency in Python for data science and machine learning (e.g., Pandas, Scikit‑learn, PyTorch or TensorFlow) and solid SQL skills.
• Experience working with Databricks and/or Spark‑based environments for scalable data processing.
• Familiarity with AWS cloud services for data access, processing, or model deployment.
• Experience with data lake or lakehouse architectures (e.g., AWS S3, Databricks), including querying and transforming large‑scale datasets.
• Experience integrating models into production environments (e.g., APIs, batch pipelines).
• Understanding of model evaluation, validation, and performance metrics.
• Strong communication skills and ability to translate analytical outputs into actionable insights.
• Experience working in cross‑functional, matrixed teams in an Agile environment.

Nice to Have

• Experience with Palantir Foundry and/or Palantir AIP, especially for AI‑enabled search or analytics workflows.
• Consulting experience strongly preferred.
• Experience building AI‑enabled search solutions, including semantic search, document retrieval, and ranking models.
• Experience with multimodal data processing, including text and image‑based analytics.
• Familiarity with OCR/ICR pipelines and document intelligence use cases.
• Experience with enterprise ML platforms (e.g., AWS SageMaker, Databricks Machine Learning) for model development, deployment, and lifecycle management.
• Experience developing explainable AI (XAI) solutions, including confidence scoring and traceability of results.
• Experience designing analytics dashboards or reporting solutions for end users.
• Previous experience supporting federal clients or working in regulated environments.
• Experience in a consulting firm and/or client‑facing delivery role.
• Experience supporting training, user enablement, or scaling analytics capabilities across teams.
• Familiarity with graph‑based analytics, ontology‑driven models, or relationship mapping.

Benefits

• Medical, dental, and vision coverage.
• Spending accounts and life insurance.
• Voluntary benefit plans.
• Participation in a 401(k) retirement plan.