Dataverse / Data Science Lead (CBP)

LMI•Remote,

10d•$111,209 - $180,317•Remote

About The Position

LMI is seeking a Dataverse/Data Science Lead to join a multidisciplinary team supporting the Department of Homeland Security (DHS) Customs and Border Protection (CBP) Program Management Office (PMO). This hands-on technical leadership role applies advanced statistical expertise, coding skills, and mission awareness to national security challenges and mission-critical capabilities through research, evaluation, and informed decision-making. The lead evaluates data quality, validates assumptions, selects analytical methods, and addresses complex technical challenges to ensure findings are accurate, defensible, and actionable. The role also strengthens team capabilities through mentorship, peer review, and consistent analytical standards, while communicating findings, uncertainty, bias, and limitations to technical, operational, clinical, and executive stakeholders. The position requires U.S. citizenship and the ability to obtain a CBP Background Investigation (BI); an active CBP BI is preferred. It requires 20% travel, which may entail travel to remote and rugged areas. The role is listed for US-Remote and Washington, DC. The target salary range is $111,209–$180,317; actual compensation may vary based on factors including location, internal equity, business considerations, client contract requirements, and candidate qualifications.

Requirements

Master’s degree or higher in statistics, data science, operations research, public health, computer science, engineering, mathematics, or another related quantitative discipline.
At least 9 years of progressive experience in applied data science, advanced analytics, statistical analysis, data engineering, or related analytical roles, including work in federal health, healthcare, regulated-data environments, enterprise modernization, or similarly complex mission-driven organizations.
Demonstrated experience leading complex analytical efforts from problem definition through data assessment, analysis, validation, interpretation, and communication of results to technical and nontechnical stakeholders.
Strong hands-on experience using Python and SQL to develop, test, validate, and troubleshoot analytical workflows, data transformations, statistical models, and machine-learning solutions.
Experience designing and assessing data models, data pipelines, data-quality controls, validation logic, data lineage, authoritative data sources, and reporting-ready datasets.
Experience with one or more advanced analytical methods, including predictive modeling, time-series analysis, causal inference, natural-language processing, machine learning, generative AI, or large language model evaluation.
Experience with modern data, analytics, and visualization platforms, such as Microsoft Dataverse, Databricks, Snowflake, Power BI, Azure, AWS, Google Cloud Platform, or comparable technologies.
Demonstrated ability to communicate analytical methods, results, uncertainty, assumptions, bias, data-quality concerns, and limitations clearly to leadership and other decision-makers.
Experience establishing or applying analytical standards for reproducibility, peer review, documentation, version control, quality assurance, validation, and responsible use of data and AI.
Demonstrated technical leadership experience, including mentoring analysts or data scientists, leading peer reviews, developing reusable analytical standards or assets, or managing complex analytical workstreams.
U.S. citizenship.
Ability to obtain a CBP Background Investigation (BI).

Nice To Haves

Active CBP Background Investigation (BI).
Doctoral degree in statistics, data science, operations research, public health, computer science, engineering, economics, epidemiology, or a related quantitative discipline.
Experience supporting healthcare, federal health, clinical analytics, public health, operations research, or other domains where data context, quality, and interpretation materially affect operational or programmatic decisions.
Experience operationalizing analytics or machine-learning solutions through repeatable data pipelines, model monitoring, MLOps practices, model lifecycle management, or collaboration with production engineering and platform teams.
Experience designing, implementing, or evaluating NLP, generative AI, or large language model solutions, including evaluation methodologies, error analysis, bias or fairness assessment, and responsible-use considerations.
Experience working with Dataverse and the Microsoft Power Platform, including Dataverse table design, relationships, security roles, integrations, data management, reporting, and Power BI analytics.
Advanced certifications in cloud machine learning, data science, analytics, data engineering, Databricks, Azure, AWS, Google Cloud Platform, Power Platform, or comparable technologies.
Evidence of recognized technical leadership through publications, conference presentations, internal analytical standards, training development, mentoring, peer-review leadership, or leadership of enterprise-scale analytical initiatives.

Responsibilities

Lead analytical strategy for complex operational, workflow, technology, and programmatic questions, aligning analyses with customer priorities and decision needs.
Serve as the lead subject-matter expert for Microsoft Dataverse data structures, data management practices, analytical use cases, and integration with reporting, automation, and data-science solutions.
Assess data readiness for analytical and reporting use cases, including data quality, completeness, sensitivity, lineage, authoritative sources, access considerations, and fitness for intended measures.
Design and maintain reporting-ready Dataverse data models, tables, relationships, transformations, validation rules, business logic, and analytical pipelines.
Develop, review, and apply statistical models, machine-learning methods, exploratory analyses, predictive analytics, and AI/large-language-model evaluations as appropriate.
Perform hands-on analysis using Python, SQL, Dataverse queries, and related tools; troubleshoot complex data, integration, transformation, reporting, and modeling issues.
Partner with program, operational, technical, evaluation, and data-governance stakeholders to establish appropriate baselines, denominators, comparisons, outcomes, performance measures, and evidence limitations.
Translate complex analytical findings into concise, actionable products for technical and nontechnical decision-makers, clearly communicating uncertainty, data-quality issues, assumptions, bias, and causal limitations.
Lead feature definition, model selection, validation strategies, error analysis, sensitivity testing, bias and fairness assessments, and interpretation for advanced analytics and machine-learning efforts.
Establish analytical standards and governance practices for reproducibility, peer review, validation, documentation, version control, data-quality checks, and transparent communication of assumptions and limitations.
Create reusable notebooks, SQL scripts, code patterns, analytical templates, Dataverse configuration standards, data dictionaries, and quality checks to improve consistency and accelerate future work.
Coach analysts, data scientists, and technical teams on data-management practices, analytical methods, quality assurance, peer review, and responsible use of data and AI-enabled capabilities.
Coordinate with data engineering, platform, application-development, and Power Platform resources on data-pipeline design, integrations, model handoff, monitoring requirements, performance expectations, security considerations, and maintainability.
Lead technical reviews of analytical plans, data models, methods, and results to ensure the data, validation approach, assumptions, and interpretation are appropriate for the customer’s decision.
Partner with data owners and governance stakeholders to improve data definitions, provenance, access patterns, quality expectations, retention practices, and responsible-use controls when recurring analysis identifies systemic data issues.
Support development of dashboards, reports, and self-service analytical products using approved reporting and visualization tools, ensuring outputs are accurate, documented, and fit for operational use.
Remain current on applicable data-governance policies, privacy and security requirements, analytical methodologies, Microsoft Dataverse capabilities, and responsible AI practices.