Summary: Quality Analytics Associates (QAA) is accepting resumes for qualified individuals to serve as the senior technical lead. The position leads evaluation and development of analytical methods for various data dependent on the project objectives. Rather than applying a predetermined algorithm, the Data Scientist/ML Lead will select and evaluate analytical approaches based on the defined decision problem and characteristics of the available evidence.
Salary: $115,000 – $120,000 (negotiable)
Responsibilities
- Serve as technical lead for statistical, predictive, and machine-learning research.
- Develop and evaluate methods for performance-trajectory analysis.
- Develop methods for point detection.
- Design predictive models and outcomes.
- Determine appropriate analytical approaches.
- Conduct feature engineering, model development, model selection, training, validation, testing, and comparative analysis.
- Establish appropriate baselines and evaluate whether advanced methods provide measurable value beyond isolated, retrospective, or threshold-based measures.
- Evaluate predictive performance, calibration, robustness, stability, useful lead time, and performance.
- Analyze false-positive and false-negative behavior and other model-error characteristics.
- Develop and evaluate approaches for uncertainty and confidence characterization.
- Implement explainability methods that identify evidence contributing to analytical outputs.
- Maintain source-to-model and source-to-indicator traceability in collaboration with the Data/Systems Engineer.
- Lead analytical-model implementation.
- Support structured evaluation of analytical outputs and incorporate human-review findings into model refinement.
- Document model assumptions, methods, performance, limitations, uncertainty, validation findings, and technical risks.
- Support demonstrations, technical reviews, and recommendations concerning subsequent model and maturation.
Required Qualifications
- Master’s degree in data science, statistics, computer science, machine learning, artificial intelligence, operations research, applied mathematics, engineering, or a closely related quantitative field; a bachelor’s degree with substantial directly relevant experience may be considered. OR Equivalent years’ experience
- Demonstrated professional experience developing statistical, predictive, and/or machine-learning models.
- Advanced proficiency with Python, R, or comparable analytical programming environments.
- Experience with common machine-learning and statistical modeling frameworks and libraries.
- Demonstrated experience with model development, validation, performance evaluation, and interpretation.
- Experience working with longitudinal, time-dependent, multivariate, or heterogeneous data.
- Knowledge of predictive modeling, classification/regression, anomaly or change detection, calibration, model robustness, and uncertainty.
- Ability to communicate complex analytical findings to technical and nontechnical stakeholders.
- Demonstrated technical leadership of data-science or AI/ML activities.
Preferred Qualifications
- Ph.D. or advanced graduate training in data science, statistics, AI/ML, operations research, human-performance analytics, or a related quantitative field.
- Experience applying AI/ML to workforce, human-performance, readiness, training, operational, or mission-support problems.
- Experience with time-series/longitudinal modeling, change-point detection, anomaly detection, survival/event analysis, forecasting, or related methods.
- Experience with explainable AI/model interpretability techniques.
- Experience with uncertainty quantification, probabilistic modeling, model calibration, and robust validation.
- Experience evaluating model performance across populations, cohorts, operating conditions, or time periods.
- Experience supporting research, prototype, analytics, or AI/ML programs.
- Familiarity with responsible AI and human-in-the-loop evaluation.
Equal Opportunity Employer: QAA will consider all qualified applicants’ employment without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other characteristic protected by law.