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Data Scientist

3 months ago

Bengaluru, Karnataka, India PayPal Full time
At PayPal (

NASDAQ:
PYPL), we believe that every person has the right to participate fully in the global economy.

Our mission is to democratize financial services to ensure that everyone, regardless of background or economic standing, has access to affordable, convenient, and secure products and services to take control of their financial lives.


Job Description Summary:

What you need to know about the role This is a pivotal Data Scientist position within the Global Merchant Lending (GML) organization at PayPal, focusing on the design and optimization of Underwriting strategies for PayPal Working Capital.

In this role, you will be at the forefront of developing innovative credit risk solutions, leveraging large and complex datasets to drive business decisions and strategy improvements.

As part of our dedicated team, you will have the opportunity to:
Develop and

Optimize Underwriting Strategies:

Utilize advanced data analytics and machine learning techniques to create, evaluate, and refine credit risk underwriting models, ensuring they align with our business objectives and risk appetite.


Work with Large, Complex Datasets:
Handle extensive datasets, extracting meaningful insights and identifying trends that influence credit risk decisions. Your expertise in SQL and Python will be instrumental in data manipulation, analysis, and model development.

Collaborate Across Teams:

Work closely with cross-functional teams, including Risk Management, Product, Engineering, and Finance, to implement and monitor underwriting strategies that enhance customer experience while managing risk effectively.

Meet our team The Global Merchant Lending (GML) team at PayPal is at the heart of managing the risk associated with our merchant lending portfolio.

Our primary focus is on the strategic development, continuous monitoring, and enhancement of risk management capabilities to ensure the robustness and growth of our lending products.


Job Description:

Your Way to Impact


In this Data Scientist role within the Global Merchant Lending (GML) team, your work will have a direct and significant impact on the success and evolution of PayPal's merchant lending offerings.


Here's how you can make a difference:

Drive Innovation in Underwriting:

By developing and optimizing data-driven underwriting strategies, you'll play a key role in shaping how we assess and manage credit risk.

Your contributions will directly influence the efficiency, scalability, and fairness of our lending decisions, enabling us to serve a broader range of merchants effectively.


Leverage Open Banking:
With your understanding of Open Banking, you'll introduce new dimensions to our risk assessment processes.

By integrating external financial data, you can help us gain deeper insights into our customers' financial health, leading to more informed lending decisions and potentially opening up new avenues for product innovation.


Collaborate for Success:
Your ability to work across teams will foster a culture of collaboration and innovation within PayPal.

By partnering with product, engineering, compliance, and finance teams, you'll ensure that our risk management strategies are not only effective but also aligned with our overall business goals and regulatory requirements.


Empower Small Businesses:

Ultimately, your work will contribute to the success of small and medium-sized enterprises (SMEs) by providing them with the financial support they need to grow and thrive.

Through your efforts in optimizing our lending solutions, you'll be directly supporting economic growth and job creation in communities around the world.


Your day-to-day


As a Data Scientist in the Global Merchant Lending (GML) team at PayPal, your daily activities will be diverse and impactful, revolving around the use of data science and analytics to drive the success of our merchant lending products.


Here's a glimpse into your day-to-day responsibilities:
-
Data Analysis and Insights: Spend a significant portion of your day analyzing large, complex datasets using SQL and Python. You'll extract actionable insights, identify trends, and uncover patterns that inform our underwriting and risk management strategies.
-
Strategy Formulation: Translate data-driven insights into actionable underwriting and risk management strategies. You'll participate in brainstorming sessions, strategy meetings, and presentations where your analytical insights will guide decision-making.
-
Performance Monitoring: Regularly monitor the performance of existing underwriting models and risk strategies, conducting thorough analyses to identify areas for improvement. You'll also develop and track key performance indicators (KPIs) to measure the effectiveness of the strategies you help implement.
-
Documentation and Reporting: Document your findings, methodologies, and model specifications clearly and concisely. You'll also prepare reports and presentations for both technical and non-technical stakeholders,