Senior Data Scientist - Credit
WFA Digital Insight
As the demand for data-driven credit decisions grows, Plaid is at the forefront of this shift. With a reported 25% increase in fintech investments in 2025, the need for skilled data scientists who can navigate complex financial datasets is more pressing than ever. This Senior Data Scientist role stands out for its unique blend of technical rigor, business acumen, and customer empathy, making it an attractive opportunity for those looking to make a tangible impact in the financial services sector. Before applying, candidates should be prepared to showcase their expertise in credit risk modeling, machine learning, and data analysis, as well as their ability to communicate complex ideas to cross-functional stakeholders.
Job Description
About the Role
The Senior Data Scientist role at Plaid is a key position within the company's credit analytics team. As a seasoned data scientist, you will be responsible for developing and implementing credit risk models, analyzing large datasets to identify trends and patterns, and providing actionable recommendations to stakeholders. You will work closely with cross-functional teams, including engineering, product, and go-to-market, to drive business growth and improve customer outcomes. The role requires a deep understanding of credit risk modeling, machine learning, and data analysis, as well as excellent communication and collaboration skills.The credit analytics team at Plaid is responsible for building and maintaining credit models that help lenders make informed decisions. The team is composed of experienced data scientists, engineers, and product managers who are passionate about using data to drive business impact. As a Senior Data Scientist, you will be an integral part of this team, contributing to the development of credit models, analyzing customer data, and providing insights to stakeholders.
The role is based in San Francisco, but remote work arrangements are available. Plaid is committed to creating a flexible and inclusive work environment that allows employees to thrive.
What You Will Do
- Develop and implement credit risk models using machine learning and statistical techniques
- Analyze large datasets to identify trends and patterns, and provide actionable recommendations to stakeholders
- Collaborate with cross-functional teams, including engineering, product, and go-to-market, to drive business growth and improve customer outcomes
- Communicate complex technical concepts to non-technical stakeholders, including product managers, engineers, and customers
- Develop and maintain technical documentation, including data dictionaries, model documentation, and process guides
- Stay up-to-date with industry trends and advancements in credit risk modeling and machine learning
- Participate in code reviews and contribute to the improvement of the codebase
- Collaborate with data engineers to design and implement data pipelines and architectures
- Develop and maintain dashboards and reports to track key metrics and performance indicators
- Provide training and support to junior data scientists and analysts
What We Are Looking For
- 4+ years of experience in a data science, credit risk, or analytical role within financial services or fintech
- Deep familiarity with SQL and Python
- Strong understanding of credit risk modeling, including machine learning and statistical techniques
- Hands-on experience building or maintaining credit policies for lenders
- Knowledge of the lending lifecycle and how to optimize across acquisition, underwriting, customer management, and collections or recoveries
- Demonstrated ability to make data-driven decisions and communicate recommendations clearly to cross-functional stakeholders
- Experience working directly with lenders or credit products and translating analysis into customer-facing recommendations
- Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams
- Bachelor's degree in a quantitative field, such as mathematics, statistics, or computer science
Nice to Have
- Familiarity with model monitoring, automation, modern data tooling, or working with large-scale financial datasets
- Experience with cloud-based data platforms, such as AWS or GCP
- Knowledge of containerization using Docker and container orchestration using Kubernetes
Benefits and Perks
- Competitive salary and equity package
- Comprehensive health, dental, and vision insurance
- 401(k) matching program
- Flexible PTO policy and remote work arrangements
- Access to cutting-edge technologies and tools
- Opportunities for professional growth and development
- Collaborative and dynamic work environment
How to Stand Out
- To stand out in this role, make sure to highlight your experience with credit risk modeling and machine learning, as well as your ability to communicate complex technical concepts to non-technical stakeholders.
- Be prepared to provide examples of your work, including code samples and technical documentation, to demonstrate your expertise.
- Show a willingness to learn and adapt to new technologies and techniques, and be open to feedback and constructive criticism.
- Emphasize your ability to work effectively in a cross-functional team environment, and highlight your experience with collaboration tools such as Slack and GitHub.
- Research the company culture and values, and be prepared to discuss how your skills and experience align with Plaid's mission and goals.
- Consider creating a personal project or contributing to open-source projects to demonstrate your skills and passion for data science and machine learning.
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