About the job: - You will architect, code and deploy ML models (from scratch) to predict credit risk. - You will design, run, and analyze A/B and multivariate tests to test hypotheses aimed at optimizing user experience and portfolio risk. - You will perform data exploration and build statistical models on user behavior to discover opportunities for decreasing user defaults. And you must truly be excited about this part. - You’ll use behavioral and social data to gain insights into how humans make financial choices - You will spend a lot of time in building out predictive features from super sparse data sources. - You’ll continually acquire new data sources to develop a rich dataset that characterizes risk. - You will code, drink, breathe and live python, sklearn and pandas. It’s good to have experience in these but not a necessity - as long as you’re super comfortable in a language of your choice. About you: - You’ve strong computer science fundamentals - You’ve strong understanding of ML algorithms - Ideally, you have 2+ years of experience in using ML in industry environment - You know how to run tests and understand their results from a statistical perspective - You love freedom and hate being micromanaged. You own products end to end - You have a strong desire to learn and use the latest machine learning algorithms - It will be great if you have one of the following to share - a kaggle or a github profile - Degree in statistics/quant/engineering from Tier-1 institutes.