Senior ML-Engineer, Finance

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