Risk Strategy Data Scientist

As a Risk Strategy Data Scientist at BILL, you will be responsible for developing and refining fraud detection and risk mitigation strategies using advanced analytics techniques. You'll leverage your experience in data science to create predictive algorithms and work collaboratively with teams across the organization to ensure effective implementation and monitoring of these strategies.

To be considered for the Risk Strategy Data Scientist role at BILL, candidates should have a minimum of 2 years of end-to-end fraud risk control experience, particularly in the eCommerce or online payments sectors. A strong background in data analytics, proficiency in SQL and Python, and the ability to collaborate with cross-functional teams are essential.

BILL is an exciting place to work because it blends innovation with a community that's committed to empowering businesses. As a Risk Strategy Data Scientist, you'll have the opportunity to influence critical financial processes and drive impactful change while enjoying a robust benefits package that includes comprehensive healthcare, flexible PTO, and employee recognition programs.

In this role, you’ll lead projects that incorporate fraud detection techniques, risk analysis, and loss mitigation strategies. You’ll engage in crafting analytical rule development, building performance dashboards, and collaborating with product and engineering teams to enhance risk signal capture and treatment strategies.

As a Risk Strategy Data Scientist at BILL, you'll utilize advanced tools like SQL, Python, and Tableau for data analysis, algorithm development, and monitoring. Your work will leverage these technologies to derive insights that inform strategic business decisions and control strategies.

The culture at BILL is collaborative, inclusive, and driven by a shared vision to empower businesses. As a Risk Strategy Data Scientist, you’ll be welcomed into a supportive environment that values diverse perspectives and emphasizes curiosity, innovation, and continuous growth.

BILL is committed to the continuous growth of its team members. As a Risk Strategy Data Scientist, you will have opportunities for mentorship, training, and professional development experiences that enhance your skills and elevate your career trajectory within the fintech space.

In responding to this question, highlight your specific experiences in developing and implementing fraud risk control strategies. Focus on particular projects where you've used data science to influence decisions and showcase any measurable results that stemmed from your analysis.

Share examples of advanced analytical techniques you've employed, such as machine learning algorithms, predictive modeling, or statistical methods. Illustrate your ability to analyze data to pinpoint risk areas and aid in the development of effective fraud mitigation strategies.

Explain your approach to fostering collaboration, emphasizing communication, shared goals, and mutual understanding of each team's needs. Provide examples of how you've previously worked with product managers and other stakeholders to implement efficient fraud risk strategies.

When answering, discuss your methodology for monitoring and evaluating model performance. Talk about how you incorporate new data trends and performance metrics to update models and ensure they effectively manage evolving risks.

Discuss your problem-solving methodologies, including how you gather data, look for insights, and approach ambiguity. Describe any specific scenarios where you successfully resolved complex data challenges in risk analysis.

Provide insights into your process of creating performance dashboards, including the tools and metrics used. Discuss how you tailor dashboards to different audiences and ensure they provide actionable insights into fraud risk.

Share specific projects where you’ve utilized SQL and Python for data extraction and transformation. Highlight your skill in building complex queries or scripts that helped clarify data for analysis in fraud detection.

When answering this question, focus on the key factors in crafting effective detection rules including risk indicators, algorithm performance, and adjustments based on testing outcomes. Provide examples to illustrate your thought process.

Provide examples of mentorship experiences you’ve had, discussing the methods you use to guide junior members in understanding complex concepts, best practices, and their professional growth in data science.

Share your enthusiasm for the rapid development of financial technologies. Discuss trends that intrigue you and how you see your role as a Risk Strategy Data Scientist at BILL contributing to innovations that benefit businesses in the fintech landscape.