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Aperio Global

Cognitive infrastructure for trusted decision-making

Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeFort Meade, MD, USA$150 - $190In-person

About the Job

Requirements

  • A bachelor's degree in a relevant discipline, such as statistics, mathematics, operations research, engineering, or computer science, and four years of experience analyzing datasets and developing analytics, plus two years of programming experience with data analysis software such as R, Python, SAS, or MATLAB.
  • A master's degree in a relevant discipline may substitute for two years of relevant experience analyzing datasets and developing analytics and one year of relevant programming experience with data analysis software such as R, Python, SAS, or MATLAB.
  • In lieu of a bachelor's degree, an additional four years of relevant experience may be substituted, for a total of eight years analyzing datasets and developing analytics and two years programming with data analysis software such as R, Python, SAS, or MATLAB.
  • For Level 2, a PhD in a relevant discipline may substitute for four years of relevant experience, reducing the requirement to four years analyzing datasets and developing analytics and one year programming with data analysis software such as R, Python, SAS, or MATLAB.
  • Candidates must be U.S. citizens and possess an active TS/SCI security clearance with a polygraph.

Responsibilities

  • Develop machine learning, data mining, statistical, and graph-based algorithms to analyze datasets.
  • Prototype or consider several algorithms and select a final model based on suitable performance metrics.
  • Build models or develop experiments to generate data when training or example datasets are unavailable.
  • Generate reports and visualizations that summarize datasets and provide data-driven insights to customers.
  • Partner with subject matter experts to translate manual data analysis into automated analytics.
  • Implement prototype algorithms within production frameworks for integration into analyst workflows.
  • Produce data visualizations that provide insight into dataset structure and meaning.
  • Collaborate with subject matter experts to identify important information in raw data and develop scripts that extract it from various data formats, including SQL tables, structured metadata, and network logs.
  • Incorporate subject matter expert input into feature vectors suitable for analytic development and testing.
  • Translate customer qualitative analysis processes and goals into quantitative formulations coded into software prototypes.
  • Develop and implement statistical, machine learning, and heuristic techniques to create descriptive, predictive, and prescriptive analytics.
  • Develop statistical tests to make data-driven recommendations and decisions.
  • Develop experiments to collect data or models to simulate data when required data are unavailable.
  • Develop feature vectors for input into machine learning algorithms.
  • Identify appropriate algorithms for datasets and tune input and model parameters.
  • Evaluate and validate analytics performance using standard techniques and metrics such as cross-validation, ROC curves, and confusion matrices.
  • Evaluate individual analytic efforts and make recommendations in the analytic development process.
  • Recommend solutions that can scale to large datasets.
  • Collaborate with software engineers, cloud developers, and stakeholders to develop production analytics.
  • Develop and train machine learning systems based on statistical analysis of data characteristics to support mission automation.

About the Company

Aperio Global

Cognitive infrastructure for trusted decision-making

Aperio Global builds the systems layer that helps organizations make confident decisions in complex, data-rich environments where AI, data, infrastructure, and missions converge. It combines Spatial, Semantic, and Environmental intelligence and uses the Cognitive Layer Framework to unify data lineage, governance, security, data fusion, and contextual reasoning into a production-grade decision platform. Its product portfolio includes NLYTEN for sense-making, CipherIQ for trust and data integrity, Reality Engine for reality capture and digital twins, and HelioShield/OmniSphere for environmental and space-weather intelligence. The company aims to reduce decision risk in national security, commercial space, enterprise AI, and immersive operations by delivering reliable, scalable, auditable decisions.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Reston, VA
Founded
2019

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