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Sr. Data Scientist
1 month ago
- Contract Role (Through January 2025)
- Hybrid role in Vienna, VA (2 days/week onsite & 3 days remote)
We are seeking a highly skilled Data Scientist to join our clients advanced analytics team on a contract basis through January 2025. This role is with a Fortune 100 Credit Union located in Vienna, Virginia. The ideal candidate will provide independent data science, machine learning, and analytical insights using member, financial, and organizational data to support mission-critical decision-making for Compliance-Complaints. This role will involve identifying opportunities for improvement in products, services, and processes to meet business objectives through the use of cutting-edge data science. The candidate will create descriptive, predictive, and prescriptive models and insights to drive impact across the organization.
Responsibilities
- 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
- Perform other duties as assigned
- Assess new and existing model's overall fit/suitability with its intended use and purpose
- Candidates are required to be legally authorized to work in the United States without a sponsoring employer indefinitely. At this time we can only accept the following: US Citizens, GC EAD, GC Holder, TN Visa.
- Must be able to work a W2 Contract (NO C2C arrangements).
- Education: Master's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or another quantitative field, or an equivalent combination of education, training, and experience.
- Problem-Solving: Ability to understand complex business problems, identify optimization opportunities, and articulate them clearly.
- Communication: Advanced skill in communicating actionable insights using data to both technical and non-technical audiences.
- Research Experience: Significant experience working in dynamic, research-oriented groups with multiple concurrent projects.
- Data Visualization: Advanced knowledge of data visualization libraries such as matplotlib or ggplot2 and other tools like Microsoft Power BI and Tableau.
- Technical Writing: Exceptional technical writing skills.
- Advanced Analytics: Advanced skills in descriptive, predictive, and prescriptive analytics and modeling, with demonstrated success in deploying impactful models.
- Modeling Techniques: Experience with various modeling types, including classification, regression, time series, clustering, text analytics, survival, association, optimization, and reinforcement learning.
- Data Techniques: Advanced knowledge of techniques like dimension reduction, natural language processing, sentiment analysis, anomaly detection, and geospatial analytics.
- Data Skills: Advanced skills in data mining, wrangling, transformation, interpretation, qualitative and quantitative analysis, and statistical research.
- Programming: Proficiency in Python, R, SQL, and querying.
- Cloud Computing: Knowledge of cloud computing technologies such as Apache Spark, Azure Data Factory, Azure DevOps, Azure ML, Hadoop, Microsoft Azure, Databricks, AWS, and Google Cloud.
- Data Concepts: Familiarity with data engineering, ETL tools, data mapping, data integration, data governance, and data warehousing.
- Data Management: Advanced skills in data management, validation, cleansing, and information analysis.
- Education: Doctoral degree in Statistics, Mathematics, Computer Science, Engineering, or another quantitative field.
- Project Management: Familiarity with project management concepts and frameworks such as Agile (SAFE), communication strategy, management, delivery excellence, and requirements management.
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