Company Name:-
PayU

Job Location:-
Bengaluru, Karnataka

Job Summary:-
Role: Data Scientist – Fraud Modeling
Location: Gurgaon / Bengaluru

About the Role: The 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.

About the team: The data sciences team is the PayU intelligence team who will be primarily responsible for modelling complex problems, discovering insights, and identifying opportunities through the use of statistical, algorithmic, mining, and visualization techniques.

Excited yet? Continue reading to find out more about the role:

What you’ll be doing:
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
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
What are we looking for?
Bachelors in engineering, mathematics, statistics , economics or a related field
2+ 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 optimization 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 c

FOR MORE DETAILS CLICK BELOW LINK [convertful id=”110657″]

Click Here For More Details