Senior Data Scientist - Risk Modeling

Description

A fast-growing fintech startup redefining access to credit in emerging markets.

We’re hiring a Senior Data Scientist to lead our risk modeling and credit decisioning efforts. You’ll work closely with local teams to design, train, and deploy models that predict risk, detect fraud, and expand financial inclusion. Expect to work with diverse data sources — app behavior, device data, and other non-traditional signals — to build models that truly understand our customers and shape the future of financial access.

The Data Scientist will lead Rise’s risk modeling roadmap, leveraging our growing datasets to build and refine predictive models that guide credit and collection decisions. This role is pivotal to the company’s success, sitting at the intersection of technology, product, and market operations.

You’ll collaborate closely with the risk, collection, and product teams, combining analytical rigor with creativity to test new hypotheses and continuously improve model performance. A curious, experimental mindset is essential — you’ll be expected to identify new data signals, validate them in-market, and scale what works.

• Develop the risk model for the platform, utilizing statistical and predictive models (e.g., logistic regression, gradient boosting, LSTM/CNN).

• Identify, extract, and engineer behavioral and contextual features from diverse data sources.

• Design and maintain scalable data pipelines for the ingestion and transformation of alternative data.

• Collaborate with product and engineering teams to ensure clean, consistent, and privacy-compliant data collection.

• Interpret model outcomes and translate results into actionable insights for risk policies and product improvements.

• Build and integrate AI agents and pipelines to enhance data features, automate experimentation, and improve model accuracy.

• Conduct deep data exploration to uncover behavioral and market trends.

• Partner with local business and operations teams to turn data insights into measurable strategies.

• Own end-to-end design, experimentation, and implementation of data projects. 
• Maintain data cleanliness, consistency, and reliability across all environments.

• A Self-starter and a highly independent individual capable of leading roadmaps end-to-end. 

• 5+ years of hands-on experience in predictive analytics.

• Proven ability to build and deploy machine-learning models for real-world decisioning

• Proficient in Python with good knowledge of data handling packages such as: numpy, pandas and statistical prediction packages: scikit-learn, xgboost, lightgbm. Strong familiarity with large-scale SQL data warehouses (e.g., Snowflake, BigQuery).

• Solid understanding of statistical and modeling techniques such as GLM, xgboost, LSTM as well as model evaluation metrics.

• Experience running A/B tests and interpreting experimental results.

• Experience with AI agents, building AI agentic pipeline and evaluation.

• Critical thinker with strong analytical and problem-solving skills.

• An independent fast learner who thrives in dynamic startup environments and collaborates effectively across functions.

• Prior experience in Fintech companies.

• Familiarity with GenAI platforms for feature generation and model enhancement.

• Experience with analytics tools (Amplitude, Tableau, Google Analytics, etc).