Data Scientist, GivingTuesday

About GivingTuesday

GivingTuesday is a global generosity movement unleashing the power of people and organizations to transform their communities and the world. The organization works with partners across sectors and borders to understand the drivers and impacts of generosity, explore giving behaviors and patterns, and use data to inspire more giving around the world. GivingTuesday offers the largest philanthropic data collaborative effort in the social sector — with unique, granular datasets from a wide range of organizations featuring key sector information

As we scale up, we are expanding our team of data scientists, researchers and engineers, who will continue to grow and improve our unique data assets, methodologies, and technical infrastructure.

In pursuit of the goals and expansion of the data commons, GivingTuesday partners with key organizations to leverage their expertise to manage and lead different aspects of the work. These data & technology partners (DARO, With Intent) manage staff, projects, and ongoing functions for the data commons with dedicated staff embedded in GivingTuesday in those capacities in cross-functional roles. This role is one of these positions - managed by our partner organizations but embedded in GivingTuesday’s Data Team.

Data Scientist

Our global data science team works on a diverse set of problems and projects related to learning, insights, and impact measurement in the nonprofit sector. We are looking for a Data Scientist to join our growing team, where they will work with data engineers, analysts, and other team members to develop compelling and useful knowledge products for GivingTuesday stakeholders, including academics, data partners, the social/nonprofit sector, and the general public.

In this role you will:

• Work with a wide range of data types including donation data, transaction records, government and census data, nonprofit tax filings, survey data on perceptions and activity, and philanthropic investment account data, gathered from collaborators and institutional partners in the nonprofit ecosystem
• Develop quarterly reports on sector-wide trends in monetary giving using transaction records
• Enhance core data and analytical pipelines by improving data quality validation, automating recurring processes, and implementing methodological updates in workflows to support evolving analytical needs
• Deliver and write analyses with actionable insights and communicate these findings to cross functional stakeholders of varying technical levels
• Manage key datasets and improve their usability by creating database dictionaries and user documentation
• Create impactful data visualisations and interactive data dashboards for stakeholders

We are looking for someone with:

• Demonstrated interest in the nonprofit and philanthropic sector and use of data to promote better social outcomes
• Advanced analytical skills in a research context, conducting exploratory analysis and mapping data flows, integration of datasets, and reviewing data sources and tools
• Experience with statistical methods including hypothesis testing, regression analysis, and sampling techniques for the purposes of social science research (such as economics, mixed methods) and/ or business analytics
• Experience working with scripting languages (Python required) and data querying languages (SQL preferred)
• Solid data visualisation skills and an aptitude for translating technical outputs into compelling stories
• Experience with software development tools and practices (e.g. version control, testing outputs, and applying QA processes)
• Understanding of legislation around privacy and best practices for securing data
• Solid relationship management skills, with the ability to collaborate with a variety of internal and external stakeholders on complex research initiatives
• Outstanding written and oral communication skills in English and an ability to communicate clearly and directly
• Attention to detail and ability to synthesise diverse datasets

GivingTuesday is actively seeking candidates with unique and diverse work backgrounds to grow our team. We are especially excited to talk to you if have:

• Programming skills: Python, PySpark, SQL, Databricks, Git, pandas
• Experience developing and maintaining analytical pipelines, including closely collaborating with Data Engineering teams
• Advanced Modelling: Regressions, Clustering, Dimensionality Reduction, Classification, Bayesian, Time-Series Analysis, prompt engineering
• Experience working with data platforms such as Databricks (or other forms of cloud data lakes/warehouses/lakehouses)
• Experience building data exploration tools using code-based frameworks (such as R Shiny or Streamlit, for example)
• An advanced degree in a quantitative research-field (definitely not required!). Non-degreed candidates must possess an extensive public record of competent, curiosity-driven data exploration on github, huggingface, kaggle, stackoverflow or similar

Location & Work Hours

Remote.

We are happy to consider applicants based in countries outside of where this is posted.

This is a full-time position. We are looking for candidates who can overlap with a 9:00 to 5:00 EST work-day, with some flexibility.

Compensation

Our compensation is competitive and tailored to align with cost-of-living differences across various regions. We look forward to meeting candidates from diverse backgrounds who can bring unique perspectives to our team!

For applicants in the US, our expected salary range is $50,000 to $70,000 USD per year.

Application Guidelines

GivingTuesday is committed to a work environment where our employees feel included, valued, and heard. If you require any accessibility accommodation in the interviewing process please let us know.

We know that applying for a job takes a lot of time and energy and we treat every application with care and attention. Only those applicants who are selected will be contacted.

To apply, please provide your resume and a short cover letter describing your interest in the position. We want to hear from you, in your own words. Submissions that reflect your personal perspective will stand out more than those written by AI tools.

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