Principal Data Scientist - Time Series

Provide analytical insights and perform analysis to assist in guiding credit risk assessments and business planning. Participate in a variety of analytical efforts to support model development, automation, business analysis, and reporting.

Deliver insights to auditors, regulators, rating agencies, model risk management, and other key decision makers. Conduct work assignments of increasing complexity under minimum supervision.

Intermediate to advanced professional within field.

• Support the delivery of strategic advanced analytics solutions across the organization with solutions drawing on descriptive, predictive analytics and modeling
• Leverage a broad set of modern technologies including Python, R, Scala, and Spark to analyze and gain insights within large data sets
• Manage, architect, and analyze big data in order to build data driven insights and high impact data models
• Evaluate model design and performance and perform champion / challenger development. Analyze model input data, assumptions, and overall methodology.
• Using statistical practices, analyze current and historical data to make predictions, identify risks, and opportunities, enabling better decisions on planned / future events
• Apply business knowledge and advanced statistical modeling techniques when building data structures and tools
• Collaborate with other team members, subject matter experts, pods, and delivery teams to deliver strategic advanced analytic based solutions from design to deployment
• Examine data from multiple sources and share insights with leadership and stakeholders
• Transform data presented in models, charts, and tables into a format that is useful to the business and aids in effective decision making
• Develop and maintain an understanding of relevant industry standards, best practices, business processes and technology used in modeling and within the financial services industry
• Identify improvements to the way in which analytics service the entire function
• Recognize potential issues and risks during the analytics project implementation and suggest mitigation strategies
• Prepare project deliverables that are valued by the business and present them in such a manner that they are easily understood by project stakeholders
• Perform other duties as assigned
• Master’s degree in Data Science, Statistics, Economics, Mathematics or another quantitative field, or related field, or the equivalent combination of education, training and experience, professional with Ph. D degree is preferred
• Significant work experience as a DS / Econometrician / Research Analyst with a focus on Econometric models and Timeseries analysis at a financial or economic research institution
• Experience in using two or more of the following modeling types to solve business problems : classification, regression, time series, clustering, text analytics, survival, association, optimization, reinforcement learning
• Demonstrates a deep understanding of the modeling lifecycle
• Advanced skills in descriptive, predictive, and prescriptive analytics and modeling; demonstrated success in building models that are deployed and have made measurable business impact
• Demonstrates functional knowledge of data visualization libraries such as matplotlib or ggplot2; knowledge of other visualization tools such as Microsoft Power BI and Tableau
• Advanced skill communicating thoughts, concepts, highly technical results / insights, practices effectively at all levels, adjusting as needed to technical and non-technical audiences
• Advanced database, word processing, spreadsheet, and presentation software skills (., Microsoft Access, Excel, PowerPoint,
• Proven experience working in a dynamic, research-oriented groups with several ongoing concurrent projects
• Advanced skill data mining, data wrangling, and data transformation with both structured and unstructured data; deep understanding of data models
• Skill interpreting, extrapolating, and interpolating data for statistical research and modeling
• Advanced skill in Python, R, and / or Scala
• Knowledge of cloud computing technologies such as : Apache Spark, Azure Data Factory, Azure DevOps, Azure ML (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud
• Working knowledge of accounting standards as they relate to Current Expected Credit Loss (CECL) standard

Hours : Monday - Friday, 8 : 00AM - 4 : 30PM

Locations : 820 Follin Lane, Vienna, VA 22180 5510 Heritage Oaks Drive, Pensacola, FL 32526