Data Scientist

Vienna, Virginia-Hybrid

Phone+Skype

6+Month

Job Description

Provide independent data science, machine learning, and analytical insights using member, financial, and organizational data to support mission critical decision making for Compliance-Complaints. Understand business needs and identify opportunities for improvements to products, services, and processes to meet business objectives through the use of cutting-edge data science. Create descriptive, predictive, and prescriptive models and insights to drive impact across the organization. Regarded as an advanced professional in the data science field. Conduct complex work under minimal supervision and with wide latitude for independent judgment. Individual contributor and mentor to junior staff.

Support the delivery of strategic advanced analytics solutions across the organization with solutions drawing on descriptive, predictive, and prescriptive analytics and modeling
Leverage a broad set of modern technologies – including Python, R, 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
Provide analytics insights and solutions to solve complex business problems 
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
Point of contact between the data analyst/data engineer and the project/functional analytics leads
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
Assess new and existing model’s overall fit/suitability with its intended use and purpose
Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering or another quantitative field, or related field, or the equivalent combination of education, training and experience
Ability to understand complex business problems and determine what aspects require optimization and articulate those aspects in a clear and concise manner
Advanced skill in communicating actionable insights using data to technical and non-technical audiences
Significant experience working in a dynamic, research-oriented groups with several ongoing concurrent projects
Demonstrates advanced functional knowledge of data visualization libraries such as matplotlib or ggplot2; knowledge of other visualization tools such as Microsoft Power BI and Tableau
Ability to manipulate raw data within visualization tools to create effective dashboards that communicate end-to-end data outcomes visually
Advanced  storytelling with data skills
Exceptional technical writing skills
Advanced skill in descriptive, predictive, and prescriptive analytics and modeling; demonstrated success in building models that are deployed and have made measurable business impact
Significant 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
Advanced knowledge of advanced techniques such as:  dimension reduction techniques, natural language processing, sentiment analysis, anomaly detection, geospatial analytics, etc.
Demonstrates a deep understanding of the modeling lifecycle
Advanced skill data mining, data wrangling, and data transformation with both structured and unstructured data; deep understanding of data models
Advanced skill interpreting, extrapolating, and interpolating data for statistical research and modeling
Advanced skill in Data Interpretation, Qualitative and Quantitative Analysis
Advanced skill in Python and R
Advanced skill in SQL and querying (able to pull/transform your own data)
Advanced 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
Understanding of data models, large datasets, business/technical requirements, BI tools, statistical programming languages and libraries
Familiar with Data Engineering concepts
Familiar with the use of standard ETL tools and techniques
Familiar with the concepts and application of data mapping and building requirements
Demonstrates a deep understanding of multiple data related concepts
Familiar with Data Integration, Data Governance and Data Warehousing
Advanced skill in Data Management, Data Validation & Cleansing and Information Analysis