Data Scientist

Candid is a nonprofit that provides the most comprehensive data and insights about the social sector. We get you the information you need to do good. Candid currently has an opportunity for a Data Scientist. Candid (candid.org), the nation’s leading authority on philanthropy, seeks a resourceful, creative, conscientious, and detail-oriented data scientist to join the Candid Data Science team. This is a generalist applied-science role, spanning work that ranges from finding and fixing data-quality problems at scale, to measuring how well our models perform in production, to exploring and prototyping new data products and derived fields.

Data scientists at this level work fairly independently on project-defined scope, often embedded with a product or data team, and partner with engineers, analysts, PMs, and subject-matter experts. We hire for a broad set of skills and deploy data scientists where they are most needed as priorities shift.

Position:Data Scientist

Reporting to: Director of Data Science

Schedule: 35-hour work week, Monday through Friday

Compensation
: $100,000 - $130,000 (this range is for the NYC area and will be adjusted for other localities; additionally, factors like skills and experience will be considered).

Location: Remote. In-person attendance is expected twice per year during our annual, weeklong all-staff summits. Additional in-person meeting participation is expected at least once per quarter for senior leaders and at least once per month for the executive team. Staff not located in the NYC area are expected to travel for these meetings.

Benefits
:
Health insurance (medical, dental, vision), retirement contribution with additional option for a match, paid life insurance and AD&D, paid leave time (PTO, compassionate leave, volunteer, holiday, parental), short-term and long-term disability, pre-tax transit, flexible spending accounts, supplemental insurance, summer hours, and Public Service Loan Forgiveness (PSLF) program eligible employer.

Responsibilities

• Apply ML across data problems: detection, classification, entity resolution and matching evaluation, anomaly detection, embeddings and semantic search, and feature engineering.
• Measure how models perform on real production data (not just test sets), build ground-truth where labels don’t exist, and detect drift and degradation.
• Apply ML to data quality: find systematic, at-scale errors that queries and manual review miss, and route findings to the people who own the fixes.
• Take open-ended questions through to a prototype and a clear recommendation, including ruling ideas out when they aren’t worth building.
• Build enabling tooling and dashboards (for example, semantic search over text, or Streamlit dashboards) that help analysts and stewards do their work.
• Work embedded with a product or data team on project-defined scope, partnering with engineers, analysts, PMs, and subject-matter experts.
• Take on other ML and data-science projects as Candid’s needs and priorities shift.

Requirements

• 3-5 years of relevant experience.
• A strong generalist applied data scientist, comfortable moving across classification, entity-resolution and matching evaluation, anomaly detection, embeddings and semantic search, feature engineering, and exploratory feasibility work.
• Strong Python and SQL, comfortable with large production datasets (Starburst/Trino, Snowflake) and working in AWS.
• Experience measuring model performance on real-world / production data (not just test sets), including building ground-truth where labels don’t exist, and detecting drift.
• Comfort building and maintaining dashboards (the team’s are in Streamlit) that surface data-quality and model-performance metrics.
• Experience taking a fuzzy question to a prototype and a recommendation, and being willing to rule an idea out.
• Sound statistical judgment: sampling, error rates, uncertainty, and knowing when a finding is real.
• Strong communication and comfort working embedded in another team with project-defined scope.
• Preferred someone with experience applying ML to data quality, such as detection or classification models that flag anomalous or wrong records at scale.
• Preferred (any of these are a plus):…