Play an integral role in supporting modeling and data analysis and database needs for assigned line of business. Manage the creation and/or usage of large data sets, providing information-based decision logic and predictive modeling solutions, and translates modeling/analytic output into understandable/actionable business knowledge, insight and applications. Demonstrate strong technical/problem solving skills. Support multiple projects collaboratively. Responsibilities
- Identify, develop and implement complex analytical solutions leveraging tools such as predictive modeling, advanced machine learning techniques, simulation, optimization solutions, etc..
- Manage dataset creation including data extraction, derived and dependent variable creation, and data quality control processes for analytics, model development, and validation. May monitor execution of analytical solutions, including criteria specification, data sourcing, segmentation, analytics, selection, delivery, and back-end data capture results.
- Under direction of the Sr. Leader, collaborate with business leaders and/or analysts to provide analytical thought leadership and support for business problems. Identify and interpret business needs, define high-level business requirements, strategy, technical risks, and scope. Develop, document, and communicate business-driven analytic solutions and capabilities, translating modeling and analytic output into understandable and actionable business knowledge.
- Embed analytic programs and tools. Ensure continued accuracy, relevancy, and effectiveness and track process improvements once deployed.
- Ensure adherence to data and model governance standards that are set and enforced by industry standards and/or enterprise and business unit data governance polices and leaders.
- Contribute to ongoing expansion of data science expertise and credentials by keeping up with industry best practices, developing new skills, and knowledge sharing. Work cross functionally to develop standardized/automated solutions and adopt best practices.
- Masters degree or equivalent (6-years) in Quantitative Discipline (i.e. Finance, Statistics, Computer Science, Actuarial Science, Economics, Engineering, etc.)
- 3 to 5 years of relevant work experience required.
- Knowledge of advanced statistical concepts and techniques; skilled in linear algebra.
- Experience conducting hands-on analytics projects using advanced statistical methods such as generalized regression models, Bayesian methods, random forest, gradient boosting, neural networks, machine learning, clustering, or similar methodologies.
- Experience with statistical programming (SAS, R, Python, SQL etc.) & data visualization software in a data-rich environment.
- Proven ability to present/communicate complex, technical materials in a way that facilitates decision making and drives outcomes; ability to communicate to less technical partners.
- Proven ability to apply both strategic and analytic techniques to provide business solutions and recommendations.
- Ability to work effectively in a collaborative team environment.
*Additional expertise/areas of specialty may be required based on business line* Preferred Qualifications
About Our Company
- Experience in IT Operations, ITSM, AIOps
- Experience with big data and advanced analytics technologies in cloud ( AWS , Azure preferred)
- Prior consulting experience
The Ameriprise Financial Technology team mission is to create innovative technology solutions and engaging digital experiences for our clients, advisors, and employees. We embrace an inclusive and collaborative culture that allows us to partner across the business and lend our expertise in the areas of corporate computing, network infrastructure and security. We celebrate the unique qualities and reward the contributions of our talented, passionate employees. If you're motivated and want to work for a strong, ethical company that cares about you and your community, take the next step with Ameriprise Technology.
Ameriprise Financial is an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, national origin, genetic information, age, sexual orientation, citizenship, gender identity, disability, veteran status, marital status, family status or any other basis prohibited by law.