Data Science Manager - Financial Services (Remote)

We are seeking a talented and driven Decision Science Manager to join our dynamic team at Flip. As a Decision Science Manager, you will play a pivotal role in driving data-informed decisions and strategies across various business areas. Your expertise in credit risk modeling, risk analytics, and data analysis will contribute to enhancing our operational efficiency, optimizing revenue streams, and ensuring a seamless customer experience.
About Flip
Rafi, Luqman, and Anjar, who were college friends in Universitas Indonesia, started Flip as a project in 2015 to transfer payments to each other at a fraction of what banks would charge them. They are pioneers in the Indonesian market, with their technology now helping millions of Indonesians, both individuals and businesses, carry out bank-to-bank money transfers through a reliable and seamless app.
After seven years of operations, Flip has helped Indonesians transfer money worth several trillions of rupiah and has received double-digit funding from respectable investors such as Sequoia India, Insight Partner, and Insignia. Flip’s ultimate mission is to give Indonesians access to one of the most progressive and fairest financial services in the world.
At Flip, we always strive to provide the fairest place for you to work, learn, and grow with talented and fun people in various opportunities to advance your career and get fair rewards. We believe that we have to treat employees, customers, and all stakeholders fairly and respectfully. Fair treatment for employees means we establish clear goals, facilitate our employees to achieve them, and value their contribution to the company with equitable benefits.
What You'll Do:
Data-Driven Insights:

• Utilize your strong data analysis skills to extract actionable insights from diverse datasets, contributing to evidence-based decision-making.
• Develop and implement sophisticated credit risk models to assess and mitigate potential financial risks, enhancing our underwriting processes and ensuring sustainable growth.

Credit Risk Management:

• Collaborate cross-functionally to establish credit risk thresholds, optimizing approval rates while maintaining acceptable levels of risk.
• Design, enhance, and maintain credit risk underwriting models for our products, ensuring robustness and accuracy in real-time and batch applications.

Process Optimization:

• Leverage your technical skills in Python, SQL, and statistical tools to automate and streamline risk assessment processes, reducing manual intervention and enhancing operational efficiency.
• Drive improvements in risk assessment timelines, allowing for faster decision-making and improved customer experience.

Business Intelligence:

• Employ your expertise in machine learning and data science to develop predictive models that optimize driver incentives, platform allocation, and other operational parameters.
• Provide actionable insights through data visualization and reporting, enabling stakeholders to make informed decisions.

Stakeholder Collaboration:

• Collaborate closely with cross-functional teams, including product, marketing, and operations, to develop and execute strategies that drive growth and minimize risk.
• Play a key role in formulating collections strategies and recovery campaigns, effectively managing default risk and improving overall portfolio performance.

What You'll Need:

• Outstanding communication skills, both written and verbal.
• Proficiency in SQL, Python, and the Microsoft Office Suite (Word, Excel, PowerPoint).
• Proven experience as a Decision Science Analyst, with a focus on credit risk modeling, data analysis, and business intelligence.
• Excellent stakeholder management skills, collaborating effectively with cross-functional teams.
• Experience in machine learning, data visualization, and statistical techniques.
• Bachelor's/master’s degree from tier 1 college
• Strong analytical mindset with the ability to translate complex data into actionable insights.
• Exceptional problem-solving abilities and attention to detail.