Sr Data Scientist

Current Employees of LendingClub: Please apply via your internal Workday Account

LendingClub (soon to be Happen Bank) is built around a simple purpose: to clear the way to help people turn intention into action, and action into financial progress. That means offering focused products, a frictionless mobile-first experience, and clear terms with no gotchas. Respect and fairness is part of our DNA, and that ideal shapes how we work, how we treat each other, and how we invest in our employees and our community. Join us in using data, bold thinking, and a commitment to innovation to help clear the way for millions of Americans to achieve more.

About the Role

Our mission at LendingClub is to empower those who strive to achieve better financial health. The Data Intelligence team builds data-driven capabilities that support business processes and decision-making across LendingClub’s lending platform.

We are seeking a Sr Data Scientist to apply advanced analytics and modeling to key decisions across the lending lifecycle. In this role, you will transform complex data into insights and decision frameworks that improve how loans are originated, approved, and managed.

You will partner with Product, Engineering, Risk, Marketing, Collections, and Operations teams to develop solutions that improve loan application flows, strengthen collections strategies, and generate operational insights.

Examples of impact areas include:
• Improving loan application flow efficiency and conversion
• Enhancing collections strategies through segmentation and forecasting
• Delivering operational insights that improve efficiency and decision quality

What You'll Do

• Develop predictive analytics and statistical solutions to support loan application, approval, and collections processes

• Design and execute data science workflows, including data exploration, feature engineering, modeling, evaluation, and monitoring

• Collaborate with Product, Engineering, Risk, Collections, and Operations teams to integrate analytical solutions into production systems and operational processes

• Design and analyze controlled experiments to evaluate product changes and operational strategies

• Build datasets and analytical frameworks that support reporting and operational insights

• Monitor solution performance and data quality in production environments

• Communicate findings and recommendations clearly to technical and non-technical stakeholders

• Mentor junior team members and contribute to best practices in applied data science

• Develop and evaluate models or AI-driven decision frameworks for prediction, segmentation, ranking, anomaly detection, or operational optimization 

• Apply GenAI or LLM-based techniques to accelerate insight generation, automate analysis workflows, or enhance internal tools while validating accuracy and business usefulness 

About You

• 6+ years of experience applying data science, statistical modeling, or advanced analytics to real-world business problems

• Strong SQL skills for querying and analyzing large datasets

• Experience using Python for data analysis and modeling, including notebook-based workflows and modern libraries

• Experience with statistical modeling, predictive analytics, and experimentation design

• Experience with feature engineering and dataset development for analytics and modeling

• Experience working with modern data platforms and cloud environments such as Snowflake, Databricks, or AWS

• Experience collaborating with data engineers and software engineers to integrate analytics into production systems

• Strong analytical thinking, problem-solving, and communication skills

• Ability to own projects end-to-end, from problem definition to measurable business impact

• Bachelor’s degree in Statistics, Computer Science, Mathematics, Economics, or a related field; or equivalent work experience

• You are fluent in applied AI and understand how to use both classical ML and modern GenAI techniques to solve real business problems. You can frame use cases, assess data suitability, select appropriate methods, evaluate performance, and communicate limitations and risks clearly.  

• You are thoughtful about responsible AI practices, including bias, explainability, monitoring, and human oversight, and you know when AI is th