Data Scientist (Recommendation systems)

<div class="content-intro"><p style="text-align: justify;">At Prodigal, we are building AI Agents for loan servicing and collections. Founded in 2018 by IITB alumni, our journey began with one bold mission: to eradicate the inefficiencies and confusion that have plagued the lending and collections industry for decades. We are backed by Y Combinator, Accel and Menlo Ventures. </p> <p style="text-align: justify;">Today, we stand at the forefront of a seismic shift in the industry, building Agentic AI applications for consumer finance. Powered by our cutting-edge platform, Prodigal’s Intelligence Engine (PIE), we’re creating the next-generation agentic workforce - one that empowers companies to achieve unprecedented levels of operational excellence and intelligence.</p> <p style="text-align: justify;">With over half a billion consumer finance interactions processed and a growing impact on more than 100 leading companies across North America, we’ve established ourselves as the go-to partner for organizations that demand more from their AI solutions. Our unparalleled experience, coupled with our trusted customer relationships, uniquely positions us to build Agentic AI applications that will revolutionize the future of consumer finance.</p> <p style="text-align: justify;">At Prodigal, we are driven by a singular, unrelenting purpose: to transform how consumer finance companies engage with their customers and, in turn, drive successful outcomes for all. </p></div><h2><strong>About the Role</strong></h2> <p>In this role you will be building recommendation systems to drive higher consumer engagement and improve performance metrics of debt collection campaigns. You would be required to wear the hat of a Data scientist to analyze patterns and convert those into model weights. In this role you will own the project end-to-end and hence we are looking for a curious and resilient DSML Engineer who thrives in a culture of experimentation and rapid iteration. Our ideal candidate doesn't just apply AI concepts – they're excited to push boundaries, challenge assumptions, and learn from both successes and failures. If you believe that the only true failure is not experimenting quickly, we want you on our team.</p> <h2>Key Responsibilities</h2> <ul> <li>Embrace a "fail fast, learn faster" mentality, aiming for a 20-30% success rate in your ideas and experiments. Continuously optimize our experimentation process, making it faster, more efficient, and more insightful</li> <li>Apply AI concepts to solve business problems, with a focus on rapid prototyping and iterative improvement</li> <li>Leverage SQL and data manipulation tools to work with complex datasets, exploring unconventional approaches</li> <li>Apply statistical methods, machine learning algorithms, and data visualization techniques to perform exploratory data analysis, embracing unexpected insights</li> <li>Build and interpret models using tools like PySpark and ML-Flow, constantly testing new approaches and parameters</li> <li>Write clean, well-documented code that facilitates collaboration and hence faster iteration</li> <li>Continuously evaluate and enhance existing models, viewing each iteration as an opportunity to learn and improve</li> </ul> <h2><strong>Technical Skills</strong></h2> <ul> <li>Proficiency in rapidly prototyping and testing ideas using large, complex datasets and SQL</li> <li>Expertise in implementing and iterating on machine learning models and algorithms, with a focus on quick experimentation cycles</li> <li>Strong programming skills in Python and/or SQL, with an emphasis on writing code that facilitates easy modification and testing</li> <li>Experience in leveraging data visualization techniques to quickly interpret and communicate experimental results</li> <li>Ability to swiftly deploy and iterate on AI solutions in cloud environments</li> </ul> <h2><strong>Soft Skills</strong></h2> <ul> <li>Passionate about experimentation and embracing failure, viewing setbacks ... (truncated, view full listing at source)