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Head of Credit remote (CA preferred)

[email [email protected] to apply]

We are looking for an experienced, innovative, and analytically driven leader to oversee Credit.

This role is responsible for optimizing predictive modeling, leveraging advanced data science techniques, and minimizing credit losses across our products.

The ideal candidate has a proven track record in credit management, risk analysis, strategic decision-making, and machine learning-driven credit solutions.

Who We’re Looking For:

• Expert in credit modeling with deep knowledge of statistical and machine learning techniques such as logistic regression, decision trees, gradient boosting, and deep learning for risk assessment.

• Proficient in data science and ML workflows, including feature engineering, model training, validation, deployment, and monitoring in production environments.

• Hands-on experience with ML-driven credit risk models, including explainable AI (XAI) methods, alternative data sources, and dynamic risk scoring models.

• Strong problem-solving skills, capable of operating across the entire credit stack—from loan-level decisions to deploying ML-based risk models and collaborating with regulators and capital partners.

• Experienced in big data technologies, working with large-scale structured and unstructured datasets for credit risk analysis.

• Proven ability to deliver models that drive significant improvements in AUC/ROC/KS and other predictive performance metrics.

• Collaborates closely with engineers, data scientists, and DevOps teams to develop scalable, automated risk management solutions.

Requirements:

• 8+ years in consumer credit modeling, risk management, and data science within a major bank, fast-growing neobank, or fintech.

• Proven ability to shape credit trends and drive data-driven decisions that impact portfolio performance.

• Strong programming skills in SQL, Python (pandas, NumPy, scikit-learn, TensorFlow/PyTorch), and experience with cloud-based ML platforms (AWS, GCP, or Azure).

• Deep expertise in statistics, experimental design, causal inference, and machine learning techniques for credit risk analysis.

• Experience with AI-driven underwriting models, fraud detection, and credit scoring algorithms using alternative data sources such as transaction, e-commerce, and behavioral data.

• Master’s or Ph.D. in Computer Science, Mathematics, Statistics, Machine Learning, or EE/ECE from a top-tier institution.

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Sr Director, Growth Marketing

We seek a dynamic leader to drive customer and merchant acquisition, engagement, and retention. You will build a high-performing team, leverage AI-driven analytics, and develop targeted offers to maximize user loyalty. This role offers the opportunity to drive exponential growth in a fast-paced, data-driven environment. 🚀

Key Responsibilities:

• Acquire and activate 2M+ customers while optimizing CAC and payback periods.

• Double customer transaction rates through segmentation, personalization, and strategic marketing.

• Develop AI-powered analytics and models to enhance marketing efforts.

• Collaborate with product and business teams to deliver the right offers at the right time.

• Build scalable, cost-effective strategies for customer reactivation and retention.

Requirements:

• Proven success in scaling high-growth products ($50M+ ARR, 100%+ YoY growth).

• Experience leading growth teams and acquiring millions of customers through multi-channel strategies.

• Expertise in analytics, CAC/LTV modeling, and ROI measurement.

• (Nice to Have): Experience in fintech or Spanish-speaking emerging markets

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Data Scientist – Credit & Fraud Risk

Salary: ~$175k, but “open for the right rockstar 🎸”

Opportunity to be an early-stage team member to drive data science innovation in credit and fraud risk management. You’ll build and deploy risk models, leverage AI/LLM tools, integrate new data sources, and collaborate with engineering for implementation.

Key Responsibilities:

• Develop and refine risk models tailored to customer segments.

• Manage and ensure data quality across large datasets.

• Assess new data sources and engage with vendors.

• Monitor and improve model performance.

• Align data science efforts with business goals.

• Optimize data infrastructure with engineering teams.

• Document model development and compliance.

Requirements:

• 5+ years in advanced statistical modeling and risk model development.

• Master’s/Ph.D. in a quantitative field.

• Expertise in machine learning, risk modeling, and LLM-based data products.

• Strong Python (sklearn, pandas, numpy) and SQL skills.

• Prior fintech or corporate underwriting is a plus.