About PayU PayU, a leading payment and Fintech company in 50+ high-growth markets throughout Asia, Central and Eastern Europe, Latin America, the Middle East and Africa, part of Prosus group, one of the largest technology investors in the world is redefining the way people buy and sell online for our 300.000+ merchants and millions of consumers. As a leading online payment service provider, we deploy more than 400 payment methods and PCI-certified platforms to process approximately 6 million payments every single day. Thinking of becoming a PayUneer and you are curious to know more about us? Read more about the life in PayU here
Roles and Responsibilities:
As a part of the Global Credit Risk and Data Analytics team, this person will be responsible for carrying out analytical initiatives which will be as follows: -
Dive into the data and identify patterns
Development of end-to-end ML models leveraging different type of data sources – Telco,
Payments, Social Media etc.
Working on Big Data to develop analytical solutions
Collaborate with various stakeholders (e.g. tech, product) to understand and design best
solutions which can be implemented
Working on cutting-edge techniques e.g. machine learning and deep learning models
Requirements to be successful in this role:
Degree (BE / B.Tech / MS, PhD or equivalent) in Computer Science, Mathematics, Operational
Research, Statistics or Natural Sciences
At least 2 years of work experience on building ML models using telecom datasets
Strong problem-solving skills with an emphasis on product development.
Work with and create data architectures.
A very clear understanding of probability and statistics, analytical approach to problem solving,and capability to think critically on a diverse array of problems
Supervised Machine Learning Algorithms: Predictive Analytics, Logistic Regression, Bayesian Approach, Decision Trees, Support Vector Machines. Bagging and Boosting algorithms – Random Forest, XGboost, Catboost, Neural Networks etc.
Understanding of advanced algorithms (i.e. Deep Learning, Probabilistic Graph Models) will be good to have
Familiarity with statistical methods such as hypothesis testing, forecasting, time series analysis, etc - gained through work experience or graduate level education
Experience with relational databases NoSQL databases such as MongoDB, Elastic Search, Redis or any graph database
Skilled at data visualization and presentation
Most importantly, an inquisitive mind, an ability for self-learning and abstraction along with a
risk appetite for experimentation and failure
Strong problem solving and understand and execute complex analysis
Experience in Python, Spark and SQL is a must
Familiarity with the best practices of Data Science
