Senior/Staff Data Scientist at Attain

Chicago, Illinois, United States -

Full Time

Start Date

Immediate

Expiry Date

02 Aug, 26

Salary

0.0

Posted On

04 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Statistical Modeling, Python, SQL, Predictive Modeling, Feature Engineering, MLOps, Credit Decisioning, Fraud Modeling, Churn Prediction, Data Exploration, Model Validation, Cloud Computing, GCP, Large Language Models, Causal Inference

Industry

Financial Services

Description

About Attain Built for consumers and companies, alike. 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. 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. 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. About the role 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. 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. Attain Office Hybrid Schedule: Chicago, IL: 4 days in-office; 1 day remote What a typical week might look like 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 Build and improve predictive models across consumer decisioning, consumer behavior modeling, fraud, churn, transaction intelligence, and other business-critical use cases Own the full model development lifecycle, including data exploration, feature engineering, model training, validation, deployment, monitoring, and retraining Develop reusable modeling pipelines, analytical tools, and production-quality code to support scalable data science work Apply strong statistical and mathematical judgment to model evaluation, calibration, robustness testing, and business impact measurement Collaborate with data analysts, engineers, product managers, and business stakeholders to deliver ML models with quality, efficiency, and precision Identify new areas where data science, predictive modeling, and optimization can improve product and business outcomes Preferred Qualifications 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 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 Demonstrated ability to apply critical thinking, causal inference, abstract reasoning, and generalization to complex, ambiguous business and technical problems Strong expertise developing, validating, deploying, and monitoring machine learning models in production 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 Experience with predictive modeling, consumer behavior modeling, risk modeling, credit decisioning, fraud modeling, churn modeling, or other high-impact applied ML use cases Solid foundation in statistics, probability, mathematics, and machine learning fundamentals Strong Python coding skills, with the ability to build models, pipelines, and analytical tools from scratch Strong SQL skills and experience working with large, messy, real-world datasets Experience with feature engineering, model evaluation, calibration, monitoring, retraining, and model performance diagnostics Experience with cloud computing services or platforms; GCP preferred Familiarity with version control, peer code review, and collaborative software development practices Demonstrated ability to learn new technologies, applications, and modeling approaches quickly Willingness to roll up your sleeves and wear multiple hats across data science, analytics, modeling, and technical execution based on business needs Strong written and verbal communication skills, including the ability to explain technical topics to both technical and non-technical audiences We’re excited to hear from you. At Attain, we are passionate about finding people to continuously help us grow our organization. We encourage you to apply, even if your experience doesn’t match every detail of the job description. If we don’t see something that immediately fits, we will keep your resume on file for future opportunities.

Responsibilities

You will design, build, and deploy predictive models to support consumer decisioning and business optimization for financial services. This involves owning the full model development lifecycle, from data exploration and feature engineering to production deployment and monitoring.