JOB SUMMARY

We are seeking a dynamic and experienced data-scientist / analyst to lead the delivery of insights, analysis, and alerting on trends and the underlying causes of quality-of-service and service-rate fluctuations in our Internet and Video products. This role plays a critical part in helping Charter consistently deliver high-quality, reliable connectivity by promptly identifying issues related to network equipment, configuration, maintenance, and other controllable factors, while also accounting for non-controllable elements such as usage and weather conditions. The insights generated will guide internal stakeholders and leadership in driving resolutions for identified issues.

This role requires applying a wide range of analytic techniques, from simple crosstabs to state-of-the-art AI/ML algorithms to measure the causal impact of drivers. In particular, we are expanding our approaches and techniques to include the fast-developing field of Causal AI, in order to precisely measure the impact of issue-drivers and estimate the potential impact of different interventions to fix these drivers. These techniques will be applied against a set of large and growing data - over 30 million rows of 100 columns per day and growing. Compute resources will include high-end relational data warehouse and cloud-based GPU-heavy compute for AI/ML. This role will be considered an intellectual co-owner of the algorithms and methods used to derive causal insights from observational data. In addition to these deep technical skills, this role will be the lead applied analyst on the team, responsible for reviewing work product, providing constructive and coaching feedback to junior team members, and delivering key results to senior stakeholders in a clear, polished, and professional manner. This position requires a strong command of statistical techniques and machine learning algorithms, as well as a demonstrated practical ability to determine where to invest time, synthesize actionable findings across diverse assignments, and present findings to audiences with diverse agendas and varying levels of technical expertise.

• Actively and consistently support all efforts to simplify and enhance the customer experience.
• Plan and lead the complete analytics lifecycle for problem solving, including: requirements gathering, problem formulation, data grooming, data exploration, model prototyping, model validation, and algorithm productionalization.
• Leverage consultative experience delivering insights on large, structured data to senior operational executives with strong communications skills (verbal, written PowerPoint).
• Perform mid-level and advanced analytics using a range of techniques from basic (pivot table) to advanced (Causal AI tools and models).
• Troubleshoot, debug, solve problems where the analytic approach, model, or tool the algorithm is not performing as well as needed, and lead the exploration of vendors in the marketplace that can meet this need, enabling buy versus build.
• Participate in reviewing vendor tools and products that might be fit for our needs; consistently keep up-to-date in the latest market and academic developments in Causal AI.
• Plan effectively to ensure analytics products are flexible, modular, and contain reusable code base.
• Identify future technical needs of assigned projects to continue to grow capabilities of organization.
• Coordinate technical activities across projects and the organization.

• Ability to read, write, speak and understand English.
• Advanced-level skills in one or more scripting, analysis, or ETL languages, ability to readily read and adapt others' code, and ability to rapidly learn new languages or techniques.
• Expert-level skills and experience with analysis language such as R and/or Python (including relevant packages) in support of advanced analytics.
• Comprehensive experience and theoretical foundation of the properties of the major families of machine learning models (regression, decision trees, clustering, SVMs, neural networks).
• Experience with modern machine learning technology and tools in order to produce model scoring code.
• Basic background (awareness) and strong interest in further developing expertise in Causal AI techniques, e.g., Paradoxes and blunders that result from lack of causal awareness, structural causal models, causal graphs, causal discovery, and causal inference. Recent ability to keep abreast of the latest developments in this field, via regular review of published work (books and papers) in this field on these topics.
• Command of advanced mathematical concepts including calculus, PDEs, probability, and statistics, and the ability to independently learn any necessary additional concepts.
• Effective synthesis and presentation skills.
• Ability to communicate results and recommendations to a wide variety of audiences including executive leadership.
• Understanding of data architecture, data warehouse and data marts.
• Experience with other database and data store technologies, such as NoSQL, key-value, columnar, graph, and document.
• Extensive experience with large data sets and the tools to obtain, transform, and store data on Big Data and streaming services.
• Program, product, or project management experience delivering analytics results.
• Comprehensive background in Linux/Unix/CentOS or Windows installation and administration.
• Ability to identify and resolve end-to-end performance, network, server, cloud, and platform issues.
• Pattern recognition and predictive modeling skills.
• Effective attention to detail with the ability to effectively prioritize and execute multiple tasks.

• Data manipulation and statistical modeling as a Scientist, Consultant, Architect, DBA, or Engineer - 8 years
• Python/SQL/ or R/SAS Programming - 8 years
• Lead the design, develop and deployment machine learning and analytics models - 3+ years

• Experience with Hadoop, HIVE, SPARK, and/or Snowflake
• Strong basic analytics skills in SQL, Excel
• Tableau and Python a plus Reading, merging, and drawing from research papers, finding the right path forward, local R&D on
  techniques, and implementing in practice
• Knowledge of other relevant tools such as SAS, SPSS, Alteryx, Linux
  Knowledge of other relevant techniques such as text analysis and text mining
• Operations-research background, in particular focused on large labor operations such as field ops, technical support, and sales
• Background with cable and/ or telecommunications

• Office environment
• Charter Technical Engineering Center
• Highly collaborative and innovative work space
• Occasional Travel