Technical Associate I, FSAS

MIT • Cambridge, MA

1d

About The Position

The Technical Associate I, FSAS, at MIT Sloan FSAS, will be instrumental in developing an extensive ETL (Extract, Transform, Load) pipeline for processing diverse datasets. This role involves identifying and validating data sources, creating robust pipelines for data extraction, processing, loading, and validation, and developing data products like violation risk scores and product classifications. The associate will also conduct research on these data products. The data sources are multilingual and include both structured and unstructured formats. As part of the Data Science subgroup, the Technical Associate I will collaborate with interdisciplinary teams to ensure data quality, consistency, and usability. Responsibilities include exploratory analyses, supporting model development, and contributing to written research outputs such as technical documentation and reports. This position offers a chance to contribute to impactful research at the intersection of data science, food safety, and public health, while gaining experience with complex datasets and advanced analytical methods.

Requirements

• Bachelor’s degree in a relevant scientific field (computer science, engineering, mathematics).
• A minimum of two years of specialized experience with qualitative data analysis, research methods, social sciences (which may include experience gained as an undergraduate).
• Familiarity using Python and SQL.
• Familiarity using Github.
• Ability to work both independently and as a member of a team.
• Attention to detail.
• Familiarity with basic statistical techniques (e.g. hypothesis testing, simple linear models).

Nice To Haves

• Experience working with databases (MSSQL).
• Experience working with Docker.
• Experience working with Selenium.
• Experience working with machine learning (TensorFlow).
• An interest in food safety and security issues.

Responsibilities

• Help create an extensive ETL [Extract, Transform, Load] pipeline to process numerous datasets.
• Identify and vet data sources.
• Develop pipelines to robustly extract, process, load, and validate data.
• Develop data products such as violation risk scores and product classification.
• Perform and write up research on these data products.
• Collaborate closely with interdisciplinary teams to ensure data quality, consistency, and usability across the project.
• Conduct exploratory analyses.
• Support model development.
• Contribute to written research outputs, including technical documentation and reports.

Benefits

• Full benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level