Senior/Staff Data Scientist, Consumer Apps - Klover

About this role

<p><strong>About Attain</strong></p> <p>Built for consumers and companies, alike. </p> <p>Klover’s engineering team powers one of the fastest-growing fintech platforms in the U.S., supporting over one million active users each month. Our systems process and move more than $1.5 billion annually, enabling real-time access to financial tools, rewards, and services that help people improve their day-to-day lives.</p> <p>As part of this team, you’ll help design, build, and scale the systems that underpin Klover’s core products and platform. You’ll work on high-impact, production-grade systems that prioritize reliability, security, and performance, and that integrate with a broad ecosystem of internal and external services. The work you do will directly shape how users interact with Klover’s products, access their money, and experience transparent, low-fee financial services.</p> <p>Klover engineers collaborate closely with colleagues across backend, frontend, data science, and product teams to deliver scalable, high-quality solutions for a rapidly growing user base. You’ll have the opportunity to work with modern technologies and architectures while helping define and evolve the next generation of inclusive, data-powered financial products—building systems and interfaces that emphasize reliability, privacy, and performance at scale.</p> <p><strong>About the role</strong></p> <p>Attain is seeking a Senior/Staff Data Scientist to support the growing needs of our suite of B2C financial services. This role will be highly hands-on, focused on building, improving, validating, and deploying predictive models that power consumer decisioning and business optimization across our app portfolio.</p> <p>You will work on advanced machine learning and statistical modeling problems, including cash-flow based credit decisioning for our earned wage advance product, Klover, as well as consumer behavior modeling, transaction categorization, paycheck detection, fraud scoring, churn prediction, and other high-impact predictive modeling use cases. The ideal candidate combines strong quantitative fundamentals with practical experience building models and analytical systems from scratch.</p> <p><strong><em>Attain Office Hybrid Schedule: </em></strong></p> <ul> <li><em>Chicago, IL: </em><em>4 days in-office; 1 day remote</em></li> </ul> <p><strong>What a typical week might look like</strong></p> <ul> <li>Hands-on development of ML and statistical models at the core of our EWA product, with a focus on fast, rigorous, and high-quality execution</li> <li>Build and improve predictive models across consumer decisioning, consumer behavior modeling, fraud, churn, transaction intelligence, and other business-critical use cases</li> <li>Own the full model development lifecycle, including data exploration, feature engineering, model training, validation, deployment, monitoring, and retraining</li> <li>Develop reusable modeling pipelines, analytical tools, and production-quality code to support scalable data science work</li> <li>Apply strong statistical and mathematical judgment to model evaluation, calibration, robustness testing, and business impact measurement</li> <li>Collaborate with data analysts, engineers, product managers, and business stakeholders to deliver ML models with quality, efficiency, and precision</li> <li>Identify new areas where data science, predictive modeling, and optimization can improve product and business outcomes</li> </ul> <p><strong>Preferred Qualifications </strong></p> <ul> <li>5+ years of direct experience working as a Data Scientist, Machine Learning Scientist, Model Developer, Applied Scientist, Economist, or similar role on relevant business problems</li> <li>Strongly preferred: Master's, or Ph.D. in a STEM field such as Computer Science, Statistics, Economics, Mathematics, Engineering, Physics, Operations Research, or a related quantitative field</li> <li>Demonstrated ability to apply critical thinking, causal inference, abstract reasoning, and generalization to complex, ambiguous business and technical problems</li> <li>Strong expertise developing, validating, deploying, and monitoring machine learning models in production</li> <li>Experience with AI/ML-assisted development tools and MLOps practices, including experience working with large language models (LLMs) or autonomous agents for code generation and model refinement</li> <li>Experience with predictive modeling, consumer behavior modeling, risk modeling, credit decisioning, fraud modeling, churn modeling, or other high-impact applied ML use cases</li> <li>Solid foundation in statistics, probability, mathematics, and machine learning fundamentals</li> <li>Strong Python coding skills, with the ability to build models, pipelines, and analytical tools from scratch</li> <li>Strong SQL skills and experience working with large, messy, real-world datasets</li> <li>Experience with feature engineering, model evaluation, calibration, monitoring, retraining, and model performance diagnostics</li> <li>Experience with cloud computing services or platforms; GCP preferred</li> <li>Familiarity with version control, peer code review, and collaborative software development practices</li> <li>Demonstrated ability to learn new technologies, applications, and modeling approaches quickly</li> <li>Willingness to roll up your sleeves and wear multiple hats across data science, analytics, modeling, and technical…