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Model Validation Remote Jobs in Washington (NOW HIRING)

A.C. Coy

Microservices Developer (Remote)

Alexandria, VA · Remote

$54.50 - $70.75/hr

This will be a 100% remote contract-to-hire position. * SELECTED CANDIDATES WITHOUT REQUIRED ... Implement input validation, error handling, idempotency, and standardized response models.

A.C. Coy

Microservices Developer (Remote)

Alexandria, VA · Remote

$54.50 - $70.75/hr

This will be a 100% remote contract-to-hire position. * SELECTED CANDIDATES WITHOUT REQUIRED ... Implement input validation, error handling, idempotency, and standardized response models.

A.C. Coy

Microservices Developer (Remote)

Alexandria, VA · Remote

$54.50 - $70.75/hr

This will be a 100% remote contract-to-hire position. * SELECTED CANDIDATES WITHOUT REQUIRED ... Implement input validation, error handling, idempotency, and standardized response models.

ICF

Manager, Data Analytics - Remote

Reston, VA · Remote

... models. The ideal candidate has demonstrated experience leading enterprise data initiatives in ... Define and enforce data quality standards, validation rules, data dictionaries, scorecards, issue ...

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Model Validation Remote information

What is the difference between Model Validation Remote vs Model Validation on-site?

AspectModel Validation RemoteModel Validation on-site
Work EnvironmentRemote, home-basedOn-site, office or client location
Required CredentialsSimilar certifications, e.g., CFA, FRM, or relatedSame as remote, often with additional in-person requirements
Industry UsageFinancial institutions, banks, asset managersSame industries, with in-person collaboration
Work FlexibilityHigh, flexible hours and locationLess flexible, fixed hours and location

Both remote and on-site model validation roles require similar credentials and industry knowledge. The main difference lies in the work environment and flexibility, with remote positions offering greater convenience and location independence, while on-site roles facilitate direct collaboration and immediate access to resources.

What are the key skills and qualifications needed to thrive as a Model Validation Remote, and why are they important?

To thrive as a Model Validation Remote, you need a strong background in quantitative disciplines such as mathematics, statistics, or finance, typically supported by a relevant degree. Proficiency with statistical software (like SAS, R, or Python), model risk management frameworks, and familiarity with regulatory guidelines (such as SR 11-7) are commonly required. Analytical thinking, attention to detail, and strong written communication are crucial soft skills in this role. These skills ensure accurate model assessments, regulatory compliance, and effective communication of complex findings to stakeholders.

What is model validation in a remote job context?

Model validation, especially in a remote setting, involves evaluating and verifying the accuracy, performance, and reliability of statistical or machine learning models from a location outside of a traditional office. Professionals in this role typically assess whether models meet regulatory requirements, function as intended, and are free from biases or errors. Remote model validators use various tools and techniques to conduct tests, write reports, and communicate findings with stakeholders via digital platforms. This work is essential in sectors like finance, insurance, and tech, where robust models drive critical decisions. Successful remote model validation requires strong analytical skills, clear communication, and proficiency with data analysis tools.

What are some common challenges faced by professionals in remote model validation roles, and how can they be addressed?

Remote model validation professionals often encounter challenges such as maintaining clear communication with model developers and stakeholders, accessing secure data environments, and staying updated with evolving regulatory standards. To address these, it's important to leverage robust collaboration tools, schedule regular check-ins with cross-functional teams, and participate in ongoing training or knowledge-sharing sessions. Establishing clear documentation protocols and ensuring secure remote access to necessary data can also help maintain productivity and compliance.
What are the most commonly searched types of Model Validation jobs in Washington? The most popular types of Model Validation jobs in Washington are:
What are popular job titles related to Model Validation Remote jobs in Washington? For Model Validation Remote jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Model Validation Remote jobs in Washington look for? The top searched job categories for Model Validation Remote jobs in Washington are:
What cities in Washington are hiring for Model Validation Remote jobs? Cities in Washington with the most Model Validation Remote job openings:
Model Validation Remote jobs near you
Data Operations Specialist (Remote)

Data Operations Specialist (Remote)

The Geneva Foundation

Bethesda, MD • Remote

$55K - $62K/yr

Full-time

Posted 13 days ago

Job description

About The Position
The Data Operations Specialist supports the integrity, accessibility, and operational use of organizational data across systems and platforms. This role is responsible for coordinating with data owners to maintain data pipelines and databases, ensuring data quality and compliance, supporting reporting and analytics needs, and partnering with cross-functional teams to optimize data workflows and enable data-driven decision-making.
This is a full time remote position that will be assigned to our corporate office in Bethesda, MD. Remote work is authorized for this position, preferably in the DC, MD, VA (NCR) area.
Salary Range
$55,000 - $62,000. Salaries are determined based on several factors including external market data, internal equity, and the candidate's related knowledge, skills, and abilities for the position.

Qualifications


Required

  • Bachler's degree in Data Science, Information Systems, Computer Science, Business Analytics, or related field; or equivalent experience.
  • 0-2 years of experience in data operations, data management, systems administration, or analytics support.
  • Proficiency with data tools such as Excel (advanced), SQL, or equivalent.
  • Experience working with enterprise systems.
  • Strong understanding of data structures, relational databases, and ETL concepts.
  • Excellent analytical, troubleshooting, and problem-solving skills.

Preferred

  • Experience in federal research, government contracting, grants management, or healthcare data environments.
  • Familiarity with data governance frameworks and concepts (e.g., lineage, stewardship, quality rules).
  • Exposure to cloud data environments (Azure, AWS, Snowflake, etc.).
  • Basic scripting experience (Python, R, PowerShell, etc.).
  • Data visualization tools (Power BI, Tableau).
  • Understanding of compliance frameworks: HIPAA, CUI, FISMA, NIST standards.

Key Competencies

  • Data accuracy and quality mindset
  • Systems and process orientation
  • Ability to translate technical issues to non-technical users
  • Strong documentation and communication skills
  • Continuous improvement and curiosity mindset
  • Organized and able to manage multiple priorities

Responsibilities

Data Management & Integrity

  • Maintain, validate, and reconcile data across enterprise systems and platforms.
  • Perform routine data audits, reconciliation, and integrity checks to ensure accuracy, completeness, and compliance.
  • Develop and enforce data standards, naming conventions, and metadata practices in alignment with organizational policies.
  • Support data documentation, version control, and audit readiness.

Data Operations & Workflow Support

  • Monitor and support data pipelines, integrations, and automated processes between platforms.
  • Troubleshoot data issues, conduct root-cause analysis, and recommend corrective actions to minimize recurring errors.
  • Document data processes, workflows, and system configuration to support scalability and cross-team knowledge transfer.
  • Partner with operational teams to identify and improve inefficient or manual data workflows.

Reporting & Analytics Enablement

  • Collaborate with Office of Strategy and Impact (OSI) to prepare clean, validated datasets for dashboards, KPIs, and operational reporting.
  • Assist with the development of data models, ETL processes, and reporting logic required for organizational insights.
  • Support data requests from internal teams, ensuring proper access, security, and compliance.

Compliance & Security

  • Ensure data handling aligns with federal, sponsor, and internal compliance requirements (e.g., NIST 800-171, HIPAA, CUI, DoD data governance).
  • Partner with cybersecurity and information governance colleagues to maintain secure data environments and role-based access controls.

Cross-Functional Collaboration

  • Work collaboratively across IGS, departmental, and analytics teams to maintain accurate, validated data and ensure that operational and technical requirements are effectively aligned and continue to provide value back to the end user.
  • Provide user support and training on data tools, dashboards, and system best practices.
  • Participate in organizational data governance working groups or committees as needed.
  • Serve as a liaison between technical and nontechnical users to translate data issues and solutions.

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