Data Scientist (Telco)

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