Model Validator

We are seeking a detail-oriented analytical and technically proficient Model Validator to evaluate test and validate machine learning models and analytical systems to ensure their accuracy reliability consistency and performance. The successful candidate will assess model outputs identify potential errors or weaknesses document findings and recommend improvements to support dependable and high-quality results.

This role is suited to a professional with strong analytical skills an understanding of data quality and the ability to work independently while collaborating with data scientists software engineers and quality assurance teams in a remote environment.

• Evaluate and validate machine learning models algorithms and analytical systems against established performance criteria.
• Review model outputs to assess accuracy consistency reliability and relevance.
• Test models using structured datasets test cases and predefined evaluation procedures.
• Identify model errors performance gaps unexpected outputs and potential sources of bias.
• Analyze validation results and prepare detailed reports documenting findings and recommendations.
• Verify data quality completeness and suitability for model evaluation.
• Conduct regression testing to determine whether model updates affect performance.
• Compare model predictions against reference data benchmarks and expected outcomes.
• Collaborate with data scientists developers and quality assurance teams to resolve identified issues.
• Maintain clear documentation of validation procedures test results and corrective actions.
• Follow established quality standards testing protocols and data governance policies.
• Recommend improvements to model testing evaluation and validation processes.
• Handle confidential datasets responsibly and follow applicable information security requirements.
• Monitor model performance against established acceptance criteria and report significant deviations.

• Bachelors degree in Computer Science Data Science Statistics Mathematics Engineering or a related field; equivalent practical experience may be considered.
• Experience in model validation software testing data analysis quality assurance or a related technical role is preferred.
• Strong analytical and critical-thinking skills.
• Understanding of statistical concepts data quality and performance measurement.
• Familiarity with machine learning concepts and model evaluation metrics.
• Proficiency in Microsoft Excel or Google Sheets for data analysis and reporting.
• Basic knowledge of Python SQL or other data analysis tools is an advantage.
• Strong attention to detail and the ability to identify inconsistencies in model outputs.
• Excellent written communication and technical documentation skills.
• Ability to follow testing guidelines and apply consistent evaluation criteria.
• Ability to work independently manage deadlines and collaborate with distributed teams.

• Experience validating machine learning predictive analytics or artificial intelligence systems.
• Familiarity with evaluation metrics such as accuracy precision recall F1-score and mean squared error.
• Knowledge of statistical testing data sampling and error analysis.
• Experience with Python libraries such as pandas NumPy scikit-learn or similar tools.
• Familiarity with model monitoring data drift and performance degradation.
• Understanding of responsible AI principles including fairness explainability and bias assessment.
• Experience with testing frameworks version control or experiment tracking tools.

• Fully remote work arrangement.
• Full-time employment.
• Working hours based on company requirements and team time zones.
• Regular virtual meetings and collaboration with technical teams.
• Ability to manage validation tasks meet deadlines and communicate findings effectively.

Compensation will depend on technical expertise relevant experience qualifications job complexity and hiring location.

Potential benefits may include:

• Paid annual leave and holidays subject to company policy.
• Remote work flexibility.
• Professional development and technical training.
• Opportunities to develop expertise in artificial intelligence and machine learning.
• Performance-based incentives where applicable.
• Health insurance and retirement benefits where offered by the employer.

The successful candidate will be expected to:

• Complete model validation tasks accurately and on schedule.
• Identify and document model errors and performance issues.
• Apply consistent and objective evaluation criteria.
• Produce clear evidence-based validation reports.
• Maintain traceable records of testing procedures and results.
• Protect sensitive data and comply with internal policies.
• Contribute to improvements in model quality reliability and testing efficiency.