Data Scientist Manager

Role Overview

• Identify, develop and implement complex analytical solutions leveraging tools such as predictive modeling, advanced machine learning techniques, simulation, and optimization solutions to support Asset Management business objectives.
• Manage dataset creation including data extraction, derived and dependent variable creation, and data quality control processes for analytics, model development, and validation.
• 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.
• Monitor execution of analytical solutions, including criteria specification, data sourcing, segmentation, analytics, selection, delivery, and back-end data capture results.
• Identify and execute targeting and optimization opportunities.
• Embed analytic programs and tools within business processes, ensuring 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 policies 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.
• Provide technical guidance and informal leadership to analytic resources, including coaching on best practices for the usage and application of analytic output, as appropriate for a manager-level role.

Requirements

• Master's degree or equivalent in Quantitative Discipline (i.e. Finance, Statistics, Computer Science, Actuarial Science, Economics, Engineering, etc.)
• 3
• 5 years relevant analytics or data science experience.
• 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.

Tech Stack

• Python
• SQL

Benefits

• Health insurance
• 401(k)
• Paid time off