Data Scientist-Vienna, Virginia-Hybrid - Stellent IT LLC

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