Senior ML-Engineer, Finance
F
Fundraise UpFinTech, Non-profit
Turkey, RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Strong English required (C1)
- Experience
- 5+ years
- Required Skills
- DockerPythonSQLGitMLFlowMongoDBAirflowClickhouseFastAPIGrafanaRedisPandasCI/CDPrompt Engineering
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
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.
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