Senior ML Engineer, Finance

This is a fully remote position, open to applicants in Portugal.

📋 Description

• Create a market intelligence database by gathering various types of data (scraping, enrichment), improving the data pipeline, and developing a machine learning model for scoring and analyzing raw data.

• Design and manage scrapers to extract essential signals from nonprofit websites, such as utilized products, payment tools, and indicators of industry verticals.

• Develop key filters, including a binary classifier for "Is this website for fundraising?", along with other features to identify high-potential prospects.

• Source and integrate financial information from international nonprofit registries, as well as third-party signals from platforms like SimilarWeb and Facebook.

• Store and organize the enriched dataset in our internal database, ensuring it is accessible and beneficial for the broader team in research and analysis.

• Collaborate closely with the sales team to understand their qualification criteria. Examine disqualified accounts in Salesforce to identify common exclusion patterns and refine the scoring process accordingly.

• Implement the scoring model and oversee the integration of outputs into Salesforce in a clean and maintainable manner.

• Develop a scraper to monitor existing clients' websites, ensuring Fundraise Up tools are effectively implemented across their platforms.

⛳️ Requirements

• Over 5 years of experience in machine learning/data science, addressing real product challenges.

• Strong proficiency in machine learning and mathematical statistics: thorough knowledge of classical algorithms (particularly gradient boosting) and familiarity with contemporary NLP/LLM methods.

• Demonstrated experience in large-scale web scraping and constructing data pipelines.

• Metrics-driven mindset: capability to connect machine learning metrics (ROC-AUC, F1, RMSE) with business metrics (conversion rate, LTV).

• Strong engineering culture: proficiency in Python with a product-focused approach; we prioritize clean code, knowledge of design patterns, and robust engineering practices.

• Advanced SQL skills; ability to independently construct complex datasets in ClickHouse and work with MongoDB.

• Understanding of MLOps: practical experience with experiment tracking and production workflows (Docker, Git, CI/CD).

• Autonomy: capacity to deconstruct ambiguous problems, select the appropriate technology stack, and deliver results to production.

🏝️ Benefits

• Private medical insurance for the employee and their family.

• 22 paid vacation days per year.

• Up to 14 paid public holidays annually.

• 5 company-paid sick leave days.

• English language courses.

• Relevant professional development opportunities.

• Gym or swimming pool access.

• Home Office Setup Assistance: the company provides support for purchasing furniture (office chair, desk, monitor) and other items to create a comfortable workspace.

• Co-working options available.

• Remote working flexibility.

• €50 monthly allowance for internet and mobile phone expenses.