Senior Data Scientist (Remote, Continental United States)

ICA.ai•Arlington, VA

3d•Remote

About The Position

ICA.ai is seeking a Senior Data Scientist to design and deliver data science and AI solutions for complex healthcare, federal health, and regulated-data environments. This role requires strong technical depth, practical judgment, and the ability to take ownership from ambiguous problem definition through evaluation and production. A Public Trust Clearance is required for the role, and we will work with you to obtain this.

Requirements

Bachelor's Degree, or equivalent work experience in Data Science or related field
Significant hands-on experience delivering data science or machine-learning systems beyond prototypes.
Strong Python and SQL skills with solid foundations in statistics, machine learning, experimentation, and data analysis.
Meaningful experience working with unstructured data, such as document processing, information extraction, NLP, text analytics, or LLM-based workflows.
Demonstrated ownership of technically complex projects, including decisions around data, architecture, evaluation, failure analysis, and production deployment.
Ability to understand how upstream data quality and extraction errors affect downstream models and decisions.
Strong analytical judgment and willingness to challenge assumptions when results do not support them.
Must be authorized to work in the United States and have lived in the US for 3 or more consecutive years.
Must be able and willing to obtain a Public Trust Clearance

Nice To Haves

Experience in healthcare, life sciences, government, regulated industries, or similarly high-accountability environments is strongly preferred.

Responsibilities

Design and build machine-learning, analytics, NLP, and generative-AI solutions using structured and unstructured data
Develop multi-stage workflows that transform complex source data—including documents and free text—into reliable, actionable outputs.
Build and evaluate downstream data-science solutions such as predictive models, prioritization, classification, analytics, and decision-support workflows.
Establish rigorous approaches to data quality, experimentation, model evaluation, validation, monitoring, and human review.
Diagnose weak-performing approaches, identify root causes, and redesign or abandon solutions when evidence does not support deployment.
Design solutions with appropriate consideration for provenance, lineage, privacy, reproducibility, data governance, and AI governance.
Work with technical teams, domain experts, clients, and leadership to translate complex requirements into defensible technical solutions.