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Remote Data Soft Logic Jobs in Virginia (NOW HIRING)

Bigbear.ai

Data Scientist

Mclean, VA · Remote

$142K - $228K/yr

Overview The Data Scientist designs and builds the v1 rule-weighted composite scoring logic that ... This position is fully remote however travel in the DMV area will be expected. Responsibilities

Bigbear.ai

Data Engineer

Mclean, VA · Remote

$142K - $190K/yr

This position is remote but will require travel in the DMV area. Responsibilities * Build source ... Develop normalization and mapping logic to translate source-specific fields into the common risk ...

Koalafi

Financial Data Analyst

Richmond, VA · On-site +1

$103K - $131K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... reporting logic * Improve internal processes, automate workflows, and leverage modern tooling ... Remote opportunities are available to candidates throughout the United States. Salary Range: $103 ...

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Remote Data Soft Logic information

What is the difference between Remote Data Soft Logic vs Data Analyst?

AspectRemote Data Soft LogicData Analyst
Required CredentialsTypically requires a background in computer science, data management, or related certificationsRequires a degree in statistics, mathematics, or related fields; often certifications like Microsoft Excel or Tableau
Work EnvironmentPrimarily remote, involving data management, software tools, and programmingUsually office-based or remote, focusing on data interpretation and reporting
Employer & Industry UsageUsed in tech, finance, and data-driven companies for software and data system managementCommon in finance, marketing, healthcare, and consulting for data analysis and decision support

Remote Data Soft Logic focuses on managing and implementing data software systems remotely, often requiring technical skills and programming. Data Analysts interpret data to generate insights, typically working with statistical tools. While both roles involve data, Remote Data Soft Logic emphasizes software and system setup, whereas Data Analysts focus on data interpretation and reporting.

Are there actually remote data soft logic jobs?

Remote Data Soft Logic jobs are available in the industry, often involving roles such as data analysis, software development, or data management that can be performed remotely. These positions typically require skills in programming, data tools, and communication, and are offered by companies embracing flexible work arrangements.

What are the most commonly searched types of Data Soft Logic jobs in Virginia?

The most popular types of Data Soft Logic jobs in Virginia are:

What are popular job titles related to Remote Data Soft Logic jobs in Virginia?

For Remote Data Soft Logic jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Remote Data Soft Logic jobs in Virginia look for?

The top searched job categories for Remote Data Soft Logic jobs in Virginia are:

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Cities in Virginia with the most Remote Data Soft Logic job openings:

Remote Data Soft Logic jobs near you

Data Scientist

Bigbear.ai

Mclean, VA • Remote

$142K - $228K/yr

Full-time

Posted 8 days ago

Job description

Overview

The Data Scientist designs and builds the v1 rule-weighted composite scoring logic that turns normalized risk signals into a transparent, defensible score. This role also prepares the scoring approach and model architecture for future interpretable ML-based scoring—ensuring explainability is preserved for adjudicator-facing workflows and audit needs. The ideal candidate blends practical applied data science with strong judgment around interpretability, traceability, and operational usability.

This position is fully remote however travel in the DMV area will be expected.


Responsibilities

  • Build and tune v1 rule-weighted composite scoring logic using normalized inputs from the common risk-signal schema.
  • Define scoring framework components (feature groupings, weights, thresholds, guardrails, and handling of missing/partial data).
  • Create interpretable explanations for scores and drivers suitable for adjudicator review (reason codes, key contributing signals, and traceable logic).
  • Design the scoring architecture to support evolution from rules/weights to interpretable ML models while maintaining auditability.
  • Prototype and evaluate interpretable model classes and explanation methods (e.g., SHAP-based explanations, constrained/monotonic models where appropriate, and rule-based hybrids).
  • Partner with data engineering and application teams to productionize scoring logic (data inputs, contracts, output formats, and performance expectations).
  • Establish validation and monitoring approaches (basic model/score QA, drift indicators, and score distribution checks).
  • Document scoring methodology, assumptions, and limitations for stakeholder understanding and accreditation/compliance artifacts as needed.

Qualifications

  • Clearance: Must maintain an active TS/SCI security clearance
  • Bachelor's Degree and 8 to 10 years of experience; Master's Degree and 6 to 8 years of experience; PhD and 3 to 5 years of experience (in lieu of Bachelor’s degree, 6 additional years of relevant experience)
  • 3–5 years of applied data science experience delivering scoring, ranking, or decision-support models.
  • Experience implementing interpretable approaches (rule-based systems, transparent composite scores, and/or explainability methods such as SHAP).
  • Strong Python skills, including scikit-learn and common data science workflows.
  • Hands-on experience with SQL for data analysis, feature development, and validation.
  • Ability to communicate scoring logic clearly to technical and non-technical stakeholders (including explaining tradeoffs between accuracy and interpretability).
  • Familiarity with adjudicative, compliance, fraud/risk, or other risk-scoring domains (preferred/bonus).
  • IC/DoD experience