Data Scientist

Responsibilities:

Provide analytical support and recommendations regarding optimization of operational processes, capital allocation, and pricing.
Provide insights into future growth and profitability strategy through historical business analysis and forecasting of industry and major customer trends (e.g. what markets to prioritize, opportunities to pursue, how to win, impact of 5G, etc.) Work with and analyze complex data from various sources.
Manipulate large data sets and navigate a variety of servers, data types, and data structures to complete statistical and other analyses
Develop predictive and descriptive models using advanced procedures like statistical modeling, linear regression and Bayesian analyses.
Required Qualifications:

Experience developing and implementing statistical models.
Experience with a statistical analysis tool like Python.
Experience performing data management, mining, and manipulation along with analytics.
Experience using SQL in a RDBMS environment.
Excellent communication skills, oral and written.
Must have strong attention to detail and ability to work independently under general instructions and with a team.
Some programming experience - This role will rely heavily on programming experience in Python, as well as SSMS
Degree (Graduate / Ph.D. preferred) in an analytical or technical field (e.g. applied mathematics, physics, computer science, operations research).Act as a liaison between consumer, network and finance stakeholders.
Develop and maintain training material.
Represent modeling on teams and task groups for projects/initiatives across the organization and act as a source of effective challenge.
Perform outreach activities to new business areas engaged in modeling • Develop and maintain business unit and enterprise-wide model risk metrics and reports •Develop and maintain model repository, develop model scorecard, and communicate model impact to senior leaders.
Lead building out action plans and milestones to drive remediation of any oversight issues.
Lead Modeling team’s communication for regulatory exams
Drive knowledge sharing on relevant industry standards, best practices, and business processes and technology used in model risk management and model development. Lead evaluation of model management technology tools.