The Enterprise Analytics Data Scientist will use advanced data modeling, predictive modeling and analytical techniques to interpret key findings from company and third party data and leverage these insights into initiatives that will support enterprise and functional outcomes.
Could be a remote position
- Works to build and continuously grow predictive analytics data systems, processes, reporting, and ultimately people to optimize business operations, provide hypothesis and simulations of suggested changes and communicate data intelligence to the organization
- Identifies solutions to business trends and problems through complex big data analysis
- Anticipates future demands of initiatives related to people, technology, budget, and business and design/implement solutions to meet these needs
- Manages and optimizes processes for data intake, validation, mining, and engineering as well as modeling, visualization, and communication deliverables
- Identify solutions by sprawling data sets and the business mindset to convert insights into strategic opportunities for our company
- Three to Five years’ directly related experience in predictive analytics required, preferably in a financial industry
- Demonstrated experience translating data into knowledge to address complex technical and strategic business needs
- Experience writing advanced code statements, models, and macros. Knowledge of various statistical software required, open-source languages such as R or Python
- Experience with Altair Knowledge Studio (Angoss / Datawatch) a plus
- Bachelor’s degree in Business, Marketing Analytics, Information Systems or related field required. Master’s degree preferred
Additional Knowledge, Skills & Abilities:
- Basic mastery and commitment to the continuous development of personal performance around the job-specific and leadership competencies necessary for this role
- Strong working knowledge of data mining principles: predictive analytics, mapping, collecting data from multiple data systems on-premises and cloud-based data sources
- Strong SQL skills, ability to perform effective querying involving multiple tables and subqueries
- Understanding of and experience using analytical concepts and statistical techniques: hypothesis development, designing tests/experiments, analyzing data, drawing conclusions, and developing actionable recommendations for business units
- Experience and knowledge of statistical modeling techniques: GLM and OLS multiple regression analyses, logistic regressions, log-linear regressions, times series regressions, variable selection and sampling techniques, decision trees, clustering and other clarification methods, etc.
- Experience working with or creating databases and dashboards using all relevant data to inform decisions.
- The ability to effectively work both independently as a contributor and collaboratively in a team is required
- Excellent communication (oral and written).
- Strong mathematical, analytical, and problem-solving skills with an ability to leverage operational insights to drive solutions
- Strong attention to detail and highly organized while focusing on work quality
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