Software Engineer, Risk / Machine Learning

New York, NY
Engineering
Full-Time
The Petal mission

At Petal, we’re using fresh thinking and cutting-edge technology to make credit honest, simple and accessible. We use machine learning to analyze more data in our credit decisions, which means more people qualify, even if they’ve never had credit before. And we use behavioral science and smart, intuitive design to offer a simple, customer-centric product that’s engineered to achieve better financial outcomes -- with straightforward terms and no fees whatsoever. The result is an exciting new approach to credit that has the potential to radically expand credit access and improve the lives of millions of consumers, shaking up a trillion dollar industry in the process.

To do this, we’re bringing together exceptional talent from across disciplines and industries to shape the future of financial services and improve the lives of our customers in a meaningful way.

The Risk Engineering team

Join a team of bright, motivated, and collaborative engineers using design patterns, machine learning, and a great attitude to wrangle complex, ever changing business rules engines under control. These mission critical systems need to evaluate accurately at all times and hold up to compliance and legal scrutiny. These systems also leverage Petal’s secret sauce to qualify applicants for credit (even without a credit report!) and help keep the business safe from fraud. To get this job done, we value open communication, diversity of thought, and a keen eye for detail. We also value cute puppies, obscure rock bands, disaster films, the word “prime”, and penguins. 

Sound interesting? Then keep reading and hit that apply button!

Key responsibilities

    • Deliver and support production grade services, including understanding and productionizing machine learning models
    • Uphold our high engineering standards by writing well-tested, production grade, fault tolerant software
    • Plan complex projects and make business vs technology trade-offs during all phases of the project lifecycle
    • Collaborate with various Petal teams (e.g. product, risk, compliance) and third party technology vendors (e.g. credit bureaus, data aggregators)
    • Develop and use a nuanced understanding of risk policy to safely implement complex decisioning logic

Characteristics of a successful candidate

    • Strong self-management, sense of ownership, and organization. Petal’s open and collaborative environment enables proactive and organized employees to really shine.
    • Collaborative, empathetic, listens with intent. Communication of complex, ever changing business and technology concepts is hard. Creating a shared understanding and path forward via an open discussion is common place at Petal. 
    • Passionate about systems design and robust software. Our underwriting decisioning systems change often as we work and experiment to bring credit to as many people as we can. We are passionate about software architecture and fault tolerant production systems. 
    • Detail oriented; strong focus on testing frameworks. Petal’s underwriting systems are complex and mission critical. We rely on strong testing frameworks and a focus on the details of requirements to deliver features correctly. 
    • Weighs trade-offs and focuses on value delivery. A fast-paced startup demands making trade-offs that balance the near term and long term value add of solutions. At Petal, we design robust systems, but try not to let the perfect be the enemy of the good.
    • Displays positivity, kindness, and humility. Our open and collaborative culture is what makes Petal a great place to work. We need more diverse people who embody our core values to make it even greater.

Nice-to-haves

    • Preferred but not required: Demonstrated expertise in Python
    • Preferred but not required: Experience with machine learning models (and running them in production)
    • Preferred but not required: Financial sector experience, notably within credit and fraud risk decisioning
    • Preferred but not required: Experience working in highly regulated industries