Job Overview

We are seeking a skilled Data Scientist to join a top US Telecom firm. This mid-level position requires 4 to 6 years of experience and is located in Bangalore, Hyderabad, or Chennai. As a full-time hybrid role, the Data Scientist will contribute to innovative projects in the telecom industry, utilizing cutting-edge technologies and methodologies.

Qualifications and Skills

• Proficiency in machine learning with practical experience in developing and implementing algorithms (Mandatory skill).
• Expertise in deep learning and neural network architectures with demonstrated project experience (Mandatory skill).
• Strong understanding of statistics and its applications to data analysis, including hypothesis testing and regression (Mandatory skill).
• Proficient programming skills in Python for data manipulation, analysis, and machine learning model development.
• Experience writing complex SQL queries for data extraction and manipulation from large datasets.
• Proven track record of developing production-grade machine learning models and integrating them into business workflows.
• Strong problem-solving skills and ability to work with large volumes of complex data, deriving actionable insights.
• Excellent communication and team collaboration skills to effectively work with cross-functional teams and stakeholders.

Roles and Responsibilities

• Design, develop, and implement scalable machine learning models and algorithms to solve business challenges in the telecom domain.
• Analyze large datasets and apply statistical methods to understand trends, patterns, and outcomes.
• Collaborate with cross-functional teams to define data requirements, business goals, and deliver actionable insights.
• Continuously evaluate and improve existing models for efficiency, performance, and accuracy using industry best practices.
• Create comprehensive documentation for developed models, including technical specifications and user guides.
• Conduct experiments and validations to iterate on model development and enhance predictive accuracy.
• Present findings and actionable recommendations to non-technical stakeholders, ensuring clear understanding and alignment.
• Stay updated with the latest advancements in machine learning and data science, integrating new techniques and tools as appropriate.