Research Assistant I

San Diego State University Research Foundation•San Diego, CA

1d•Onsite

About The Position

The successful candidate will collaborate within a multidisciplinary team, contributing domain knowledge and technical expertise to develop, implement, and maintain artificial intelligence (AI) and machine learning (ML) models and applications. This role involves working across the data pipeline—from data acquisition and preprocessing to model development and evaluation—while supporting broader analytical needs within the team. The position requires strong problem-solving skills, attention to data quality, and the ability to translate complex data into actionable insights.

Requirements

None

Nice To Haves

Prior experience in developing end-to-end machine learning pipelines or applications.
Familiarity with cloud computing platforms (e.g., AWS, Google Cloud, Azure).
Experience with version control systems (e.g., Git).
Domain knowledge relevant to the team’s focus area.
Candidate must reside in California and live within a commutable distance from SDSU at time of hire.

Responsibilities

Develop and implement automated data extraction pipelines from structured and unstructured data sources (e.g., databases, APIs, web scraping).
Ensure data integrity, consistency, and proper documentation of data sources and workflows.
Maintain and update datasets to support ongoing model development and analysis.
Perform data cleaning, transformation, and normalization to prepare datasets for analysis and modeling.
Handle missing, inconsistent, or noisy data using appropriate statistical and computational methods.
Engineer and select relevant features to improve model performance.
Design, build, and optimize machine learning and statistical models for predictive and/or descriptive tasks.
Select appropriate algorithms based on problem type, data characteristics, and performance requirements.
Conduct hyperparameter tuning and model optimization.
Evaluate model performance using appropriate metrics (e.g., accuracy, precision/recall, RMSE, AUC).
Perform cross-validation and robustness checks to ensure generalizability.
Document model assumptions, limitations, and performance outcomes.
Conduct exploratory data analysis (EDA) to identify trends, patterns, and anomalies.
Generate visualizations and summaries to communicate findings effectively.
Support decision-making by translating analytical results into actionable insights.
Work closely with team members, including domain experts and stakeholders, to understand project requirements and objectives.
Assist with ad hoc data analysis requests and contribute to ongoing research or product development efforts.
Participate in team meetings, code reviews, and documentation practices.
Maintain clear and comprehensive documentation of data pipelines, modeling processes, and analytical workflows.
Ensure reproducibility of analyses and models through version control and best practices.
Other Duties as assigned