Data Scientist-Core Business Services-Investments-Supervising Associate-Multiple Positions-1480403

EY focuses on high-ethical standards and integrity among its employees and expects all candidates to demonstrate these qualities.  At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.  

Data Scientist, Core Business Services (Investments) (Supervising Associate) (Multiple Positions), Ernst & Young U.S. LLP, Tysons (McLean), VA.

 

Develop and maintain programming for data pipeline, perform data exploration, work on model testing, improve model performance, and achieve model optimization. Analyze and interpret datasets to identify significant differences in relationships, and to support the development of analytic solution features. Build econometric and machine learning models to predict business performance and provide impactful business insights in key areas, such as financial forecasting, scenario planning, employee attrition and productivity, and client account performance drivers, which will provide key inputs to critical businesses processes. Work closely with executives responsible for creating and driving data driven solutions within EY and our clients. Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software.
 
Provide technical guidance and share knowledge with team members with diverse skills and backgrounds. Consistently deliver quality client services focusing on more complex, judgmental and/or specialized issues. Demonstrate technical capabilities and professional knowledge. Learn about EY and its service lines and actively assess and present ways to apply knowledge and services. Communicate results effectively to leadership by translating complex models into business-friendly tools. Mentor and technically develop a junior team member.

 

Full time employment, Monday – Friday, 40 hours per week, 8:30 am – 5:30 pm.

 

 

MINIMUM REQUIREMENTS:

 

Must have a Bachelor’s degree in Data Science, Business Analytics, Economics, Finance, Mathematics, Statistics, Computer Science, Information Technology, Computer Engineering or a related quantitative field and 3 years of related work experience.  Alternatively, will accept a Master’s degree in Data Science, Business Analytics, Economics, Finance, Mathematics, Statistics, Computer Science, Information Technology, Computer Engineering or a related quantitative field and 2 years of related work experience.

 

Must have 1 year of experience applying advanced tree-based machine learning algorithms, including Gradient Boosting for classification problems.

 

Must have 1 year of experience with dimension reduction and working with financial data. 

 

Must have 1 year of experience with data augmentation techniques with at least one or a combination of the following: SMOTE, SMOTE-NC, and/or SMOGN.  

 

Must have 1 year of hands-on experience implementing statistical techniques including OLS, or Logit.

 

Must have 2 years of experience in one or a combination of the following programming languages used for data cleaning, analysis, and visualization: R, Python, Stata, and/or Julia.

 

Must have 2 years of experience with SQL and common relational database tools, including one or a combination of the following: MS SQL; MySQL; and/or Azure SQL Database.

 

Must have 2 years of experience creating dashboards and visualized metrics using Tableau and/or Power BI.

 

Must have 2 years of experience in: 

• independent task management and distilling project objectives in a new environment of data sets;
• addressing business needs and creating successful technology solutions within budget, timeline, and quality;
• managing projects that involve predicting level of attrition of a group of employees, preparing the data set with multiple descriptors related to demographic characteristics and work specific characteristics.

 

Must have 2 years of experience manipulating, transforming, and analyzing complex data including all of the following:

• Analyzing exploratory data;
• Checking for data quality;
• Applying statistical tests;
• Conducting distributional analysis;
• Conducting feature engineering;
• Testing multicollinearity in both continuous and categorical variables;
• Checking for outliers;
• Applying data augmentation techniques;
• Applying Machine Learning Algorithms and/or statistical techniques for analysis;
• Measuring the