Data Scientist - Telco

Bangalore, India
Payment – Payments India - Data Science /
Full-time /

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 bestsolutions 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 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, Redisor 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