Data and Statistical Analyst
Department: CAS Psychology
Part/Full Time: Full Time
Advertised Salary Range:
About UofSC
From the Upstate to the Lowcountry, the University of South Carolina system is transforming the lives of South Carolinians through the impact of our eight institutions and 20 locations throughout the state. More than 50,000 students are enrolled at one of eight institutions, including the research campus in Columbia and comprehensive four-year universities in Aiken, Upstate and Beaufort. In addition, our Palmetto College campuses in Salkehatchie, Union, Lancaster and Sumter enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build healthier, more educated communities in South Carolina and beyond.
Advertised Job Summary
To ensure the ESD Lab remains on track with the five -year NIH grant timeline, a dedicated Data and Statistical Analyst is required to manage the critical transition from raw data to high-impact results. This role is essential for bypassing the technical backlogs that often delay papers and reports. Working closely with the Principal Investigator (PI) and Research Associates, the analyst will provide the specialized computational support needed to execute machine learning (ML) aims, handling large, complex datasets that exceed the capacity of general staff. By integrating daily data cleaning with advanced modeling, this position directly helps the team meet grant milestones and ensures the lab stays in constant compliance with modern NIH Data Management and Sharing (DMS ) standards.
Required Education and Experience
Preferred Qualifications
• Technical Expertise: Proficiency in R or Python for longitudinal modeling (e.g., Latent Growth Curve Modeling or Mixed-Effects models) to track developmental changes over 36 months.
• Machine Learning: Experience applying ML to heterogeneous datasets (e.g., combining behavioral scores, clinical observations, and environmental variables) to predict diagnostic outcomes.
• Domain Knowledge: Familiarity with standard autism assessments (e.g., ADOS -2, M-CHAT , Mullen Scales) and the unique challenges of “noisy” behavioral data in infants and toddlers.
• NIH Compliance: Expertise in formatting and uploading data to the National Database for Autism Research (NDA ), which has specific, rigorous requirements for every six-month data submission cycle.
• Data Cleaning: Specialized skills in handling missing data and attrition, which are common in 3-year longitudinal studies with families and young children.
Knowledge/Skills/Abilities
• Advanced Programming: Expert-level proficiency in R (Tidyverse, lme4) or Python (Pandas, Scikit-learn) for reproducible data pipelines.
• Trajectory Modeling: Ability to perform growth curve analysis, mixed-effects modeling, and latent class growth analysis to track development over 36 months.
• Machine Learning Implementation: Skill in building and validating models (e.g., Random Forests, XGBoost, Clustering) to identify early risk factors for autism.
• Data Harmonization: Expertise in cleaning and merging multi-source data (e.g. behavioral scores, environmental exposures, and medical records.)
• Data Visualization: Ability to create clear, publication-ready visuals that “translate” complex ML findings for the PI and research team.
Job Close Date
06/05/2026
Special Instructions to Applicant
Positions are advertised for a minimum of five (5) business days on our job website. After five (5) business days, positions can be closed at the discretion of the department at any time. This employment site is updated on a regular basis. The length of the recruitment and screening process may vary from position to position, depending upon a variety of factors. Should review of your qualifications result in a decision to pursue your candidacy, you will be contacted by phone or email. We are only accepting applications submitted by June 5, 2026.
To apply, please visit: https://apptrkr.com/7097379
EEO Statement
The University of South Carolina does not discriminate in educational or employment opportunities on the basis of race, sex, gender, gender identity, transgender status, age, color, religion, national origin, disability, sexual orientation, genetics, protected veteran status, pregnancy, childbirth or related medical conditions.
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