Technical Analytics Manager / Lead Data Scientist
New
This is a remote position.Full-TimeManager
Salary not disclosed
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Job Details
- Experience
- Minimum of 5 years of hands-on experience
- Required Skills
- PythonSQLMachine LearningData visualizationRNLP
Requirements
- Minimum of 5 years of hands-on experience developing analytic rules and models for fraud detection, waste and abuse detection, or investigative analytics.
- Minimum of 5 years of hands-on experience designing analytic approaches, managing model development, and conducting quality-control reviews.
- Minimum of 5 years of experience tracking project progress and mitigating risks and issues in analytics projects.
- Minimum of 5 years of hands-on experience coding analytic rules and models using open-source programming technologies.
- Experience working with complex, large-scale, structured and unstructured datasets.
- Proficiency with Python, R, SQL, or comparable analytics and programming technologies.
- Experience developing and validating predictive models, anomaly detection solutions, or other advanced analytics capabilities.
- Strong leadership, analytical, problem-solving, and project-management skills.
- Strong written and verbal communication skills for presenting to technical and non-technical audiences.
- Ability to successfully complete and maintain the required government background investigation.
Responsibilities
- Lead the design, development, testing, validation, and deployment of advanced analytics and machine learning solutions.
- Manage analytics projects supporting fraud detection, prevention, waste and abuse identification, and investigative activities.
- Develop and evaluate predictive models, anomaly detection methods, risk models, and entity-resolution techniques.
- Identify and refine analytics use cases in collaboration with government stakeholders, investigators, and program teams.
- Conduct technical and quality-control reviews of analytics work products before delivery.
- Provide technical leadership, mentoring, and guidance to data scientists, analysts, and other project personnel.
- Present analytical findings, technical recommendations, and project updates to technical and non-technical stakeholders.
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