Company
Cox Communications, Inc.Job Family Group
Job Profile
Management Level
Flexible Work Option
Travel %
Work Shift
Compensation
Compensation includes a base salary of $117,300.00 - $195,500.00. The base salary may vary within the anticipated base pay range based on factors such as the ultimate location of the position and the selected candidate’s knowledge, skills, and abilities. Position may be eligible for additional compensation that may include an incentive program.Job Description
Cox Communications is looking for a Lead Data Scientist to join the Data Science team supporting Cox Business. This role will be responsible for designing and implementing advanced analytics along with managing third-party data scientists.
PRIMARY RESPONSIBILITIES AND ESSENTIAL FUNCTIONS:
Design, implement, test, prototype, and deploy ML (machine learning), DL (deep learning), AI (artificial intelligence), and NLP (natural language processing) applications to extract value from Cox Communications structured and unstructured data assets.
Develop and maintain robust data pipelines to support ML/AI workflows, including data ingestion, preprocessing, transformation, and feature engineering from various data sources.
Perform end-to-end statistical analyses, from problem definition through data extraction, transformation, and insight generation.
Conduct ad-hoc analyses to meet specific and emerging business needs.
Collaborate with team members to address complex, previously unresolved challenges and improve data flow and processing efficiency.
Leverage insights and experience from past projects to support decision making and challenge assumptions from diverse perspectives.
Assist with or lead development of industry whitepapers and other technical publications.
Guide junior team members in Data Science and support contractors and third-party data scientists during the ML modeling process.
Identify and implement opportunities for automation in data processing, feature extraction, and ML workflows to drive knowledge discovery, decision making, insights, optimization, and new capabilities.
Develop frameworks and apply best practices to mature analytics and data engineering solutions from proof of concept to production.
Prepare documentation, reports, and visualizations to communicate findings effectively and ensure knowledge transfer.
Continuously evaluate and integrate new technologies, tools, and data sources to enhance data infrastructure, storage, and processing capabilities.
QUALIFICATIONS AND EXPERIENCE:
Bachelor’s degree in Data Science, Machine Learning, Statistics, Mathematics, Computer Science, Operations Research, Analytics or a related discipline, and 6 years’ experience in a related field. The right candidate could also have a different combination, such as a master’s degree and 4 years’ experience; a Ph.D. and 1 year of experience; or 18 years’ experience in a related field
5+ years of experience in advanced analytics roles, including ML, AI, NLP, research, predictive analytics, and similar areas.
Strong programming skills (5+ years of experience) and proficiency in various data/analytic software, languages, and tools, including Spark (Spark SQL), R (caret, ggplot2), Python (pandas, numpy, scipy, scikitlearn, VennAbers, Mapie), Scala, Java, C++, Hive, SQL, Tableau, and others.
Expertise in machine learning and deep learning frameworks such as TensorFlow, Keras, Caffe, MXNet, and Pytorch, with a focus on developing complex programs, custom algorithms, and optimized model performance.
Extensive practical knowledge in: Cloud computing; Jira; GitHub; and production processes in cloud.
Experience developing, implementing, maintaining ML/AI solutions, and managing end-to-end data pipelines using cloud-native tools in AWS, with specific expertise in SageMaker for model training and deployment, Athena for querying large datasets, S3 for data storage and management, and EMR for big data distributed processing.
Experience in designing end-to-end ML pipelines, including data ingestion, ETL, feature engineering, and model deployment.
Experience with Hadoop and big data clusters for high volume data handling, transformation, and ETL optimization
Experience in implementing MLOps practices to ensure efficient model deployment, monitoring, and lifecycle management, facilitating collaboration between data science and operational teams.
Deep understanding of the mathematical and computational concepts behind advanced analytics algorithms.
Strong communication skills, with the ability to translate complex analytics into actionable business insights and communicate effectively within diverse teams.
A curious mindset focused on innovation and continuous learning to drive data and analytics improvements.
Preferred:
5+ years of programming experience in Python, PySpark, and SQL for scalable data processing, with expertise in building and optimizing data pipelines from raw data ingestion through model deployment.
Practical experience in Generative AI techniques (e.g., large language models, GANs) applied to transaction reduction, customer experience, and predictive analytics.
Experience in data visualization using tools like Tableau, Power BI, or Plotly to convert data into actionable insights, supporting industry specific applications in telecommunications, cable, high tech, and consulting sectors.
Benefits
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