General Summary: 

The Data Scientist at the Stone Living Lab (SLL), UMass Boston is responsible for curating, managing and generating ways to make SLL data publicly accessible in a way that obeys FAIR (Findability, Accessibility, Interoperability, and Reuse) best practices. The Data Scientist will work with PIs, partners, and students to create, organize and maintain SLL data management processes as well as generate public-facing products using SLL data. This role will be housed within the School for the Environment and will work on SLL projects.

The Data Scientist will play a crucial role in preparing SLL data for major repositories (e.g. the Environmental Data Repository (https://edirepository.org/), DataONE (https://www.dataone.org/services/), PANGAEA Data Publisher for Earth & Environmental Science (https://www.pangaea.de/, NOAA National Centers for Environmental Information (NCEI), IOOS/NERACOOS), to provide long-term public availability, robust metadata generation, and open APIs. They will work collaboratively with diverse stakeholders, including community organizations, government agencies, academic partners and internal UMB partners to ensure that SLL’s data is managed to the highest standards and communicated widely and equitably to support climate resilience.

Examples of Duties: 

1. Data Management and Infrastructure

• Design and implement robust data pipelines for ingestion, validation, transformation, and storage that follow best practices for the international data community.
• Develop or maintain database systems (e.g. PostgreSQL, cloud storage, APIs) to ensure efficient access and querying.
• Work with scientists to ensure standardization and interoperability across different projects (e.g., consistent formats, metadata standards).
• Automate data collection and cleaning processes from instruments, sensors, or external sources.
• Advise on data privacy and ethical considerations, especially for public-facing platforms.

2. Data Curation and Quality Control

• Work with scientists (for domain specifics) to perform data validation and anomaly detection to ensure integrity and consistency.
• Work with scientists to create and maintain metadata documentation for datasets (descriptions, provenance, usage guidelines).
• Create archiving protocols and pipelines for future use.
• Manage version control of datasets and data schemas.
• Establish best practices for data governance, archiving, and FAIR principles (Findable, Accessible, Interoperable, Reusable).
• Maintain clear, accessible documentation for datasets, codebases, and analytical methods.
• Create pipelines between SLL data and the Ocean Biogeographic Information Service (OBIS) and the Global Ocean Observing System (GOOS) for international representation of SLL data.

3. Data Analysis and Interpretation

• Assist researchers to analyze datasets to support scientific objectives and extract insights from ecological, biological, or environmental questions.
• (Depending on domain knowledge) Use statistical and machine learning techniques where appropriate to model trends or forecast outcomes.

4. Public-Facing Information and App Development Support

• Work with scientists and the education team to build data-driven applications, dashboards, or interactive tools for the public, stakeholders, or decision-makers.
• Work with real-time data streams to establish data pipelines that quality control data before providing to the public.
• Translate research outputs into visualizations, maps, or summaries accessible to nontechnical audiences.
• (Depending on experience) Contribute to API design to ensure external users can access selected data securely and intuitively.

5. Cross-Disciplinary Collaboration 

• Serve as a bridge between scientists, the education team, and external partners.
• Help define data needs for different users (researchers, decision-makers, public).
• Train lab members in data literacy and reproducible science workflows (e.g., R/Python scripting, GitHub).

6. Documentation and Reporting

• Contribute to scientific publications, reports, and grant proposals by providing data summaries and figures.
• Assist in tracking data contributions to performance metrics or impact assessments (e.g. number of downloads, reuse in policy tools).

Qualifications: 

C. Minimum Qualifications:

• Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field.
• 2+ years of experience with data science or machine learning in a professional setting.
• Experience working with real-world data, including data cleaning, transformation, and exploratory analysis.
• Familiarity with statistical analysis.
• Strong understanding of climate change impacts and environmental/climate justice.
• Strong communication and organizational skills.
• Strong problem-solving skills and ability to work independently and with diverse stakeholders.
• Excellent written and verbal communications skills.

D. Preferred Qualifications:

• Advanced degree (MSc or PhD) in a quantitative discipline.
• Experience contributing to or maintaining open-source projects.
• Demonstrated ability to design and build ML pipelines and APIs for scalable deployment.
• Familiarity with machine learning techniques.
• Familiarity with Ecological Meta-Language (EML).
• Experience with R Shiny, Python or others.
• Knowledge of web content management systems interfacing with relational database systems using common programming languages.
• Familiarity with major data hosting platforms for ecological or other environmental data streams (e.g., Data One, OBIS, EDI, or others).
• Experience with tabular, imagery, raster, video, and other data formats.
• Publications or technical writing in machine learning or data science.

Application Instructions: 

Please apply online with your resume, cover letter and list of three professional references.

Review of candidates will begin following the application closing date. 

Only Internal candidates in the Professional Staff Bargaining Unit will be considered during the first 10 business days of the posting.  All other candidates will be considered after that period. 

Salary Ranges for the appropriate Pay Grade can be found at the following link: 

Grade:  31            

Salary Ranges

This is an exempt union position. This is a grant funded position with a current end date of May 31, 2026, subject to renewal contingent on funding and university needs.

All official salary offers must be approved by Human Resources.