Data Scientist - Telco

Bangalore, India

Payment – Payments India - Data Science /

Full-time /

Hybrid

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

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