Data Scientist (Telco)
PayU
- Gurgaon, Haryana Bangalore, Karnataka
- Permanent
- Full-time
- 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
- Degree (BE /
- • 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