Sr Data Scientist

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

LendingClub Corporation (NYSE: LC) is the parent company of LendingClub Bank, National Association, Member FDIC. We are the leading digital marketplace bank in the U.S., having helped our nearly 5 million members secure over $90 billion in loans to refinance high-cost debt and achieve their financial goals. Members today have mobile-first access to a growing range of products and services designed to work seamlessly together to deliver value in new ways. Everyone deserves a better financial future, and our team is committed to making that a reality. Join the Club!

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 k