Fundraise Up is a modern fundraising platform built to make donating to nonprofits as fast and convenient as possible. They are seeking a Senior ML Engineer to own a high-impact client intelligence initiative, focusing on data collection, modeling, and production deployment to enhance their sales pipeline.
Responsibilities:
- Build a market intelligence data-base via collecting different types of data (scraping, enrichment), fixing data pipeline and creating an ML model for scoring and analysis of the raw data
- Design and operate scrapers to extract key signals from nonprofit websites, including products used, payment tools, and industry vertical indicators
- Develop critical filters such as an "Is this website for fundraising?" binary classifier, alongside other features that distinguish high-potential prospects
- Source and integrate financial data from international nonprofit registries, as well as third-party signals from SimilarWeb and Facebook
- Store and structure the enriched dataset in our internal database, making it accessible and useful across the broader team for research and analysis
- Work closely with the sales team to understand their qualification criteria. Analyze disqualified accounts in Salesforce to identify common exclusion patterns and refine scoring accordingly
- Deploy the scoring model and own the process of integrating outputs into Salesforce in a clean, maintainable way
- Build a scraper to monitor existing clients' websites, tracking whether Fundraise Up tools are correctly implemented across their properties
Requirements:
- 5+ years of ML/DS experience solving real product problems
- Strong expertise in ML and mathematical statistics: solid knowledge of classical algorithms (especially gradient boosting) and understanding of modern NLP/LLM approaches
- Proven experience with large-scale web scraping and data pipeline construction
- Metrics-driven mindset: ability to connect ML metrics (ROC-AUC, F1, RMSE) with business metrics (conversion rate, LTV)
- Strong engineering culture: confident in Python with a product-oriented approach; we value clean code, knowledge of design patterns, and solid engineering practices
- Advanced SQL; ability to independently build complex datasets in ClickHouse and work with MongoDB
- MLOps understanding: hands-on experience with experiment tracking and production workflows (Docker, Git, CI/CD)
- Autonomy: ability to break down ambiguous problems, choose the right tech stack, and deliver to production
- Curiosity and a hypothesis-driven mindset
- Ability to communicate complex analytical concepts to non-technical audiences
- Detail-oriented with a strong sense of ownership
- Comfort working in fast-paced, data-rich environments