PayU - Data Scientist (3-6 yrs)
We are looking for a 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 to utilize online payments data and analytics to solve business 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 utilising the traditional datasets and graph data.
- Apply advanced statistical and predictive modelling 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.
- Utilise patterns and variations in the volume, speed and other characteristics of data for predictive analysis.
- Extending company's data with third party sources of information when needed
- Creating automated anomaly detection systems and constant tracking of its performance
- Collaborate with various stakeholders (e.g. tech, product) to understand and design best solutions which can be implemented
Skills and Qualifications:
- Bachelors in engineering, mathematics, statistics, economics or a related field.
- 3+ years of work experience as a Data scientist preferably in a fintech environment
- 1+ years of work experience on fraud modelling
- Proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimisation algorithms.
- Proficient in machine learning (SVM, GLM, boosting, random forest) and deep neural networks
- Strong programming skills (Spark or other big data frameworks, R, Python), statistical modeling (R, Python, SAS), query languages such as SQL
- Familiarity with basic principles of distributed computing and distributed databases.
- Strong problem solving skills to understand and execute complex analysis
- Familiarity with the best practices of Data Science