Finance Data Science Advisor ( SME – Financial and IT Modeling & Analytics

Finance Data Science Advisor ( SME – Financial and IT Modeling & Analytics)

3-5 days per week onsite – Washington, DC

NO C2C or 3rd Party

The Finance – Data Science – Advisor role will offer you the flexibility to make each day your own, while working alongside people who care so that you can deliver on the following responsibilities:

• Use advanced mathematical, analytical, or econometric tools to create algorithms and analyses that will be used to support the Multifamily CECL and DFAST/forecasting processes within the Finance organization.
• Research and evaluate model results and perform credit-related analyses for both expected and stress scenarios including CECL/DFAST.
• Coordinate team activities with product and/or business owners, data engineers, and platform teams to understand business needs and current capabilities, data availability, and alternative uses to drive success of parts of projects, programs, or products.
• Ensure application of statistical modeling capabilities from disciplines, such as computer science, computational science and methods, statistics, econometrics, data optimization, and data visualization.
• Build predictive analytic capabilities within the team to enhance the delivery of business applications, and support the integration of data and statistical models or algorithms.
• Apply innovative industry practices in research and testing to product development, deployment, and maintenance.
• Oversee the design and build of new modeling applications to support risk measurement, financial valuation, decision making, and business performance for parts of products or initiatives.
• Ensure the team communicates complex ideas and solutions effectively to business partners through data visualizations, technical documentation, and non-technical presentation materials.

• 6 years

• Bachelor degree or equivalent
• A Masters degree or equivalent in Data Science, Applied Economics, Statistics, or other similar graduate studies
• Familiarity with advanced techniques including machine learning and natural language processing (NLP)
• Prior quantitative and finance training, including forecasting/stress testing (DFAST) knowledge.
• Ability to direct and evaluate the more technical aspects of data analysis and research, while also managing team in a timely manner to focus on the broader context and key impacts of the analysis
• Ability to build and maintain strong business relationships with partners
• Strong written and verbal communication skills
• Relationship management including managing and engaging stakeholders, partners, customers, and building relationship networks
• Strong analytical and problem-solving skills to conduct and manage analysis to address complex business problems
• Programming including coding, debugging, and using relevant languages such as Python, R, SQL, or similar
• Experience in the process of analyzing data to identify trends or relationships to generate business insights and inform conclusions about the data
• Expertise in visualizing data to identify, summarize and explain observed data patterns

• R programming
• Tableau
• Python
• SQL
• DBeaver SQL client software
• Bit Bucket