Senior ML-Engineer, Finance

Highlights: Role: Senior ML-Engineer, Finance Location: Spain, Remote Language: Strong English required (C1) About Us Fundraise Up is a modern fundraising platform built to make donating to nonprofits as fast and convenient as possible. We continuously innovate to reduce page load times, boost conversion rates, and support a wide range of payment methods. Each month, people around the world contribute tens of millions of dollars through our platform. The world s leading nonprofit organizations trust Fundraise Up. UNICEF, the most prominent UN charity, uses our platform for 100% of its online fundraising. So does the American Heart Association, the Alzheimer s Association, and many others. We re proud to maintain a 4.9 out of 5 rating on leading review platforms. We serve the enterprise segment, with a primary client base in the US, Canada, UK, and Australia. The Team Our product development team is currently at 150+ and growing. Team members are located across Spain, Serbia, Poland, Portugal, Turkey, Cyprus, Georgia and Armenia. We primarily communicate in Russian. We re a tight-knit, high-impact team where every task matters. It s a dynamic, collaborative environment where smart, curious engineers support one another, share knowledge, and strive for excellence. We encourage open dialogue and host bi-weekly engineering meetups to explore technical topics and showcase team insights. About the Role We're looking for an ML Engineer with 5+ years of production experience to own a high-impact client intelligence initiative. Following a successful proof-of-concept with an external consultant, we are bringing this project fully in-house. The ultimate goal is to generate a comprehensive, enriched list of all potential clients globally - understanding their product requirements, industry verticals, and overall revenue potential - and deploy a scoring model that feeds directly into our sales pipeline. This is an end-to-end ownership role. You will build from the ground up: data collection, enrichment, modeling, and production deployment. The project is co-managed by company executives and has a high strategic value. What You ll Do • 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. Challenges You'll Navigate • At the scale of ~1 million domains, expect domain duplicates, inconsistent data, and significant noise. You'll need to develop robust, cost-efficient filtering pipelines. • A single model won't cover everything - you'll likely build several targeted sub-models tailored to specific verticals and geographies as the project matures. • All of this needs to be accomplished without incurring high infrastructure or data costs. Pragmatic, scrappy solutions are valued here. 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 Our Tech Stack Core: Python (uv, ruff), FastAPI, Pydantic, Docker Models: CatBoost, Uplift Modeling (CausalML), OpenAI...