RESEARCH DATA SPECIALIST II

Job Description and Duties Under the general direction of the Workforce Data Officer, the Research Data Specialist II designs, organizes, and conducts independent and objective statistical analyses that can serve as evidence of discrimination and identify potential systemic violations of civil rights laws enforced by the California Civil Rights Department (CRD). The incumbent provides advice and guidance to attorneys, subject matter experts, and other staff on collecting data, designing studies, and interpreting statistical analyses for the investigation or litigation of matters involving discrimination in employment, housing, and other areas to support CRD’s mission. The incumbent serves as an internal statistical expert in employment and housing discrimination cases and may be called upon to testify in court on behalf of CRD. In July 2025, the State of California implemented the Personal Leave Program (PLP) 2025. This program temporarily reduces employees’ monthly base pay in exchange for paid leave credits. This position is subject to PLP 2025 and will have a monthly base pay reduction of 3% in exchange for 5 hours of leave credits through June 2027. The salary ranges shown on this job posting do not reflect the salary reduction. You will find additional information about the job in the Duty Statement. Special Requirements We review your job application to determine if you meet the minimum qualifications. Please ensure to provide detailed information in your application. Desirable Qualifications In addition to evaluating each candidate's relative ability, as demonstrated by quality and breadth of experience, the following factors will provide the basis for competitively evaluating each candidate: • PhD in a quantitative social science (e.g., demography, economics, industrial/organizational psychology, sociology) or related quantitative field (e.g., econometrics, statistics). • 5 years of relevant work experience in quantitative research related to demography, industrial organization, labor economics, microeconomics, statistics, or a related field. • Demonstrated experience designing and conducting statistical studies to support investigations and litigation of employment and/or housing discrimination. • Demonstrated expertise and experience in econometrics, mathematical demography, microeconomics, statistical inference, statistical modeling, and/or survey methodology. • Demonstrated expertise in statistical programming languages (e.g., Python, R). • Demonstrated experience working with large or complex datasets and relational databases. • Demonstrated ability to lead research end-to-end with minimal guidance.?? • Demonstrated experience in effective communication of statistical analyses and results to diverse audiences. • Demonstrated ability to distill research findings to drive strategic thinking and inform decision-making. • Experience testifying, consulting, and providing litigation support on economics, statistics, and related issues. Statement of Qualifications Statement of Qualifications A Statement of Qualifications (SOQ) is required and must be submitted with your application. Applications packets received without an SOQ will not be considered. The SOQ is a narrative description of how an applicant’s education, experience, knowledge, skills, and abilities meet the qualifications for the position. The SOQ serves as documentation of an applicant’s ability to present information clearly and concisely in writing. • Describe how your education, training, skills, and experience uniquely qualify you for this position. • Describe your experience and approach to translating broad, ambiguous policy questions or legal theories into testable quantitative hypotheses. Provide examples. • Describe your experience designing an empirical strategy to produce objective statistical evidence of discrimination that withstood cross-examination, adversarial critique, or rigorous review. Provide examples. • Describe your experience and protocol for working with flawed, incomplete, or unstandardized administrative data provided by adversarial parties (e.g., defendants in litigation). How do you handle and validate the data to ensure resulting estimates were robust and bias-free? Provide examples. • Describe your experience and approach to translating complex statistical modeling into clear, credible, and actionable insights for non-technical stakeholders. Provide examples.