Data Scientist - Artificial Intelligence/Machine Learning

The ideal candidate should possess a Bachelor's Degree in Computer Science or B.Stat and a Master's Degree in Computer Science, AI/ML, Data Science, or Statistics. A PhD in one of these fields is highly desirable. Candidates with a Master's degree should have a minimum of 6 years of relevant industry experience, while those with a PhD should have at least 2 years of industry experience. The candidate must have excellent knowledge of Data Structures and Algorithms, with a focus on efficiently solving complex problems through optimized approaches. Strong programming skills in Python are required, with hands-on experience in developing AI/ML algorithms and working on data analysis projects. A working knowledge of C++ is essential for integrating algorithms into product pipelines. Extensive experience in designing, developing, and deploying AI/ML solutions is necessary. The candidate should also have experience in applying data science techniques such as classification and clustering, with a strong understanding of statistical methods. Proficiency in working with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) is required for solving advanced problems. Understanding and experience in working with Large Language Models (LLM) for natural language processing tasks are also important. The primary responsibility of the candidate will be to design and develop AI/ML algorithms to address complex technical challenges, specifically in the context of 3GPP standards. They will leverage AI to provide innovative solutions for telecom technologies. The candidate will also be responsible for documenting the design and functionality of developed data science and AI/ML algorithms and presenting the algorithms and their results to technical and non-technical stakeholders. Collaboration with cross-functional teams is essential to ensure the seamless integration, productization, and deployment of AI/ML models into the product pipeline. Efficient management of all stages of the AI/ML model lifecycle, from development to deployment, is also a key responsibility.,