Data Scientist analyzing claims data, optimizing processes and collaborating across departments at Allianz Spain. Utilizing statistical techniques and developing predictive models for operational efficiency.
About the role
- Data Scientist Manager analyzing data for marketing and risk management at Ameriprise Financial. Identify and implement analytical solutions while collaborating cross-functionally.
Responsibilities
- Identify, develop and implement complex analytical solutions leveraging tools such as predictive modeling, advanced machine learning techniques, simulation, optimization solutions, etc., with respect to credit card, lending and cash products for marketing and risk management purposes.
- 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.
- Consult and coordinate campaign execution for direct to client campaigns.
- Identify and execute targeting and optimization opportunities.
- 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.
- Ensure adherence to data and model governance standards that are set and enforced by industry standards and/or enterprise and business unit data governance policies and leaders.
Requirements
- Master's degree or equivalent in Quantitative Discipline (i.e. Finance, Statistics, Computer Science, Actuarial Science, Economics, Engineering, etc.)
- 3 - 5 years relevant 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.
Benefits
- vacation time
- sick time
- 401(k)
- health insurance
- dental insurance
- life insurance
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