Mavenir is building the future of networks and pioneering advanced technology, focusing on the vision of a single, software-based automated network that runs on any cloud. As the industry's only end-to-end, cloud-native network software provider, Mavenir is transforming the way the world connects, accelerating software network transformation for 250+ Communications Service Providers in over 120 countries, which serve more than 50% of the world’s subscribers.

Role Summary

What will you do
• Applied data science research to fight spam, scam and fraud attacks in SMS, MMS, e-mail and other mobile telecommunication protocols
• Helping mobile network operators worldwide in localization, identification, monetization and prevention of spam and fraud attacks
• Big Data analysis of Voice/SMS/MMS traffic (>100 million messages per day)
• Data cleaning and preprocessing (data wrangling), exploratory analysis, statistical analysis
• Machine learning, data mining, text mining in different languages
• Data visualization and presentation
• Uncovering activities of organized groups of spammers and fraudsters
• Researching new fraud techniques and designing algorithms for their detection and prevention
• Monitoring and preventing virus and malware distribution vectors in SMS/MMS
• Presenting results to customers, leading discussions about findings and best approaches to manage the fraud attacks
What will you work with
• Statistical tools – R-studio, python
• Mavenir’s solution for identification of fraud and spam in mobile networks
• Unique data sets (Voice/SMS/MMS/RCS communication from all around the world)
• State of the art fraud detection algorithms
• Core mobile network systems and technologies
• Linux OS
• Big data tools - Spark, ElasticSearch/OpenSearch, Kafka
• Data science and machine learning tooling - NumPy, SciPy, MLlib

Key Responsibilities

What will you do
• Applied data science research to fight spam, scam and fraud attacks in SMS, MMS, e-mail and other mobile telecommunication protocols
• Helping mobile network operators worldwide in localization, identification, monetization and prevention of spam and fraud attacks
• Big Data analysis of Voice/SMS/MMS traffic (>100 million messages per day)
• Data cleaning and preprocessing (data wrangling), exploratory analysis, statistical analysis
• Machine learning, data mining, text mining in different languages
• Data visualization and presentation
• Uncovering activities of organized groups of spammers and fraudsters
• Researching new fraud techniques and designing algorithms for their detection and prevention
• Monitoring and preventing virus and malware distribution vectors in SMS/MMS
• Presenting results to customers, leading discussions about findings and best approaches to manage the fraud attacks
What will you work with
• Statistical tools – R-studio, python
• Mavenir’s solution for identification of fraud and spam in mobile networks
• Unique data sets (Voice/SMS/MMS/RCS communication from all around the world)
• State of the art fraud detection algorithms
• Core mobile network systems and technologies
• Linux OS
• Big data tools - Spark, ElasticSearch/OpenSearch, Kafka
• Data science and machine learning tooling - NumPy, SciPy, MLlib

Job Requirements

What we expect you already know/have

• Practical experience with statistical analysis or Business Intelligence
• Scripting languages (for example R, bash, python, perl, lua or similar)
• Data visualization and reporting
• Critical thinking and strong problem-solving skills
• Curiosity and willingness to learn new things
• Working proficiency in English

We appreciate you already know/have

• Machine learning
• Linux

Accessibility

Mavenir is committed to working with and providing reasonable accommodation to individuals with physical and mental disabilities. If you require any assistance, please state in your application or contact your recruiter.

Mavenir is an Equal Employment Opportunity (EEO) employer and welcomes qualified applicants from around the world, regardless of their ethnicity, gender, religion, nationality, age, disability, or other legally protected status.