Upgrade · Posted 4w ago
Senior Data Scientist (Flex Pay)
Job relayed by Stapply Data, its source: you apply there.
- Location
- 🇺🇸 San Francisco, United States
- Contract
- Full-time
- Role
- Data science
- Source
- Stapply Data
Stack
- Spark
- Pandas
- NumPy
- Tableau
- Power BI
- scikit-learn
- SQL
- Python
The posting
Join our team as a Senior Data Scientist, where you will build and maintain credit and fraud policy simulators, deploy statistical models and machine learning algorithms, and analyze large datasets. You will collaborate with cross-functional teams and stay up to date with the latest trends in data science and fintech. Enjoy benefits such as a stock option plan, 100% paid medical, dental, and vision insurance, flexible PTO, paid parental leave, and opportunities for professional growth and development.
- Construire et maintenir des simulateurs de politiques de crédit et de fraude pour garantir le bon fonctionnement des systèmes. - Développer et déployer des modèles statistiques et des algorithmes d'apprentissage automatique pour résoudre des problèmes commerciaux. - Analyser de grands ensembles de données structurées et non structurées en utilisant SQL, Python ou des outils similaires.
- Experience and/or strong interest in machine learning techniques (Random Forest, Gradient Boosted Trees, etc.) strongly preferred - Advanced Degree (MS/PhD) in Data Science, Statistics, Mathematics, Computer Science, Finance, or a related quantitative discipline - Proactive, driven, and ability to work in a fast paced environment - 3-5+ years of hands-on experience in a data science or analytics role, preferably in financial services - Ability to write documentation and present analysis to people with different levels of expertise (e.g., technical staff, business leads, etc.) - Strong proficiency in Python (Pandas, Numpy, Scikit-learn) and SQL - Experience with AI tools such as Claude - Experience with data visualization tools (e.g., Tableau, Power BI) - Understanding of financial services concepts such as credit scoring, portfolio risk, or customer lifetime value - Experience with data technologies like PySpark