Role: Data Scientist
Location preference: Mumbai / Bangalore
About PayU A Prosus group company
PayU is the payments and fintech business of Prosus, a global consumer internet group and one of the biggest investors in the fintech space globally, with investments totalling $700 million- to date. In India, PayU serves more than 350,000 merchants with 70+ local payment methods and is the preferred payments partner for nearly 60% of the e-commerce merchants, including all leading e-commerce companies and a majority of airline businesses.
PayU specializes in credit products and services for emerging markets across the globe. Headquartered in Mumbai, PayU Credit India has disbursed about 20 billion loans to date by volume and has disbursed more than $280 million in consumer credit. PayU Credit provides consumers with easy access to affordable short-term loans up to INR 5 lacs that can be availed in the form of personal loans, point of sale EMI and express loans. Lead by Prashanth Ranganathan, the company has been focusing on providing innovative solutions that make access to credit convenient as well as quick. PayU India entered the alternate lending business in 2017 with LazyPay, its buy-now-pay-later offering to provide consumers with convenient checkout options. It emerged as one of the most popular payment methods across 300+ renowned merchants in its network like Swiggy, Zomato, Samsung, Dunzo, GoAir, amongst others. Recently the company launched ‘LazyPay Credit Shield’ an end to end digital lending features that help its users to be mindful of their financial health and credit score and automates the entire process of disbursal. Riding on the success of the offering in 2018, PayU received RBI’s approval to operate as an NBFC in 2018. PayU acquired a stake in PaySense in January 2020 to accelerate its vision for credit in India and leverage AI & ML to build a full-stack digital lending platform in India. PayU in its next phase of growth is developing a full regional fintech ecosystem providing multiple digital financial services in one integrated experience.
We are looking for a senior data scientist in the PayU intelligence team who will be primarily responsible for modeling complex problems, discovering insights, and identifying opportunities through the use of statistical, algorithmic, mining, and visualization techniques. Your primary focus will be to propose innovative ways utilizing graph databases and analytics to look at the problems by applying data mining techniques, doing statistical analysis, validating your findings using an experimental and iterative approach, and building high-quality prediction systems integrated with our services. You will need strong business understanding, analytical and problem-solving skills, and programming knowledge.
Design experiments, test hypotheses, and build models utilizing the traditional datasets and graph data.
Apply advanced statistical and predictive modeling techniques to build, maintain, and improve on multiple real-time decision systems.
Identify what data is available and relevant, including internal and external data sources, leveraging new data collection processes such as geo-location or social media
Utilize patterns and variations in the volume, speed and other characteristics of data for predictive analysis.
Define the preprocessing or feature engineering to be done on a given dataset, data augmentation pipelines, training models and tuning their hyperparameters, analyzing the errors of the model and designing strategies to overcome them
Selecting features, building and optimizing classifiers using machine learning techniques
Extending company’s data with third party sources of information when needed
Creating automated anomaly detection systems and constant tracking of its performance
Skills and Qualifications
Bachelors in mathematics, statistics or computer science or a related field; Masters or PHD degree preferred.
3+ years of relevant quantitative and qualitative research and analytics experience.
Extensive knowledge of statistical techniques.
Ability to come up with solutions to loosely defined business problems by leveraging pattern detection over potentially large datasets.
Proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization algorithms.
Proficient in deep learning (CNN, RNN, LSTM, attention models, etc.), machine learning (SVM, GLM, boosting, random forest), graph models and reinforcement learning
Experience with open source tools for deep learning and machine learning technology such as Keras, tensorflow, pytorch, scikit-learn, pandas, etc.
Strong programming skills (Hadoop MapReduce or other big data frameworks, Java, Python), statistical modeling (R, Python, SAS), query languages such as SQL, Hive, Pig
Familiarity with basic principles of distributed computing and distributed databases.
Demonstrable ability to quickly understand new concepts - all the way down to the theorems - and to come out with original solutions to mathematical issues.