Data Scientist

Responsibilities

• Performs data collection, analysis, validation and reporting.
• Designs, tests, and documents processes, SQL queries, and stored procedures.
• Extracts and analyzes data from various sources, including databases, manual files, and external websites.
• Responds to data inquiries from various groups within an organization.
• Creates and publishes regularly scheduled and ad hoc reports.
• Documents reporting requirements and processes and validates data components as required.
• Requires experience with relational databases and knowledge of query tools and/or statistical software.
• Designing and executing complex data analysis activities, including feature engineering, data exploration, data cleansing, and dimensionality reduction to extract actionable insights from large, diverse datasets.
• Prototyping, building, and deploying predictive machine learning models (e.g., regression, classification, time series analysis) to improve network reliability.
• Translating data insights into actionable recommendations for improving network and service reliability.
• Partnering with cross-functional teams to understand business needs, identify relevant data sources, and present findings in a clear and concise manner.
• Demonstrate analytical and problem-solving skills with attention to detail and data accuracy.
• Forward thinking to stay updated on emerging data science techniques and contributing to the development of data science infrastructure and best practices.
• Strong analytical and organizational skills are essential.
• Mentor junior engineers and provide technical guidance to the team.
• Perform other duties as requested.

• Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent work experience.
• Minimum of 10 years of overall engineering work experience.

• Proficiency in Python and related data science libraries (Pandas, Num Py, Scikit-learn) for data manipulation and model building.
• Deep understanding of statistical concepts (hypothesis testing, regression analysis, time series analysis) and ability to apply them to real-world problems.
• Experience with advanced machine learning algorithms (e.g., neural networks, decision trees, clustering, regression analysis, ensemble methods) and model evaluation techniques.
• Skillful use of data visualization tools to effectively communicate complex insights to diverse audiences.
• Excellent written and verbal communication abilities to clearly explain technical concepts to non-technical stakeholders including designing reporting dashboards, or charts/graphs to easily provide insights for higher level non-data heavy stakeholders.
• A working knowledge of modern IP networks, preferably with cable industry exposure, is a nice to have.