Decision Science Analyst Lead – Life Company (Remote)

Key Skills

Data Analysis, Data Mining, Data science techniques, Data Visualization, Insurance Sales, Java Programming, Kaggle, Machine learning techniques, Neural Networks, Python Programming, R Programming, SAS, Sensor fusion Algorithms, SQL

Job Description

We are currently seeking a talented Decision Science Analyst Lead within the Life Company. The primary focus of this role will be to support Life Company member segmentation with providing insights and advice to Life Company leadership. In addition to insights delivery, the individual selected for this position will be the point of contact as the Life Company representative for supporting enterprise efforts surrounding the member segmentation efforts. This position is open to working Remote or a Hybrid schedule from the San Antonio, Phoenix, Colorado Springs or Tampa campuses.

Provide decision support for business areas across the enterprise. Staff in this area will be responsible for applying mathematical and statistical techniques and/or innovative /quantitative analytical approaches to draw conclusions and make 'insight to action' recommendations to answer business objectives and drive change. The essence of work performed by the Decision Science Analyst involves gathering, manipulating and synthesizing data (e.g., attributes, transactions, behaviors, etc.), models and other relevant information to draw conclusions and make recommendations resulting in implementable strategies.

Job Requirements

Tasks:

• Identifies and manages existing and emerging risks that stem from business activities and the job role.
• Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled.
• Follows written risk and compliance policies, standards, and procedures for business activities.
• Leverages expert business, analytical and technical knowledge to lead discussions with cross functional teams to understand and collaborate highly complex business objectives and influence solution strategies.
• Applies advanced analytical techniques to solve business problems that are typically large scale with significant impact to current and/or future business strategy.
• Organizes and oversees the development of requirements/resources necessary to meet established objectives
• Applies innovative and scientific/quantitative analytical approaches to draw conclusions and make recommendations to answer the business objective and drive the appropriate change. 
• Translates recommendation into communication materials to effectively present to all levels of management.
• Incorporates visualization techniques to support the relevant points of the analysis and ease the understanding for less technical audiences.
• Identifies and gathers the relevant and quality data sources required to fully answer and address the problem for the recommended strategy through testing or exploratory data analysis (EDA).
• Integrates/transforms disparate data sources and determines the appropriate data hygiene techniques to apply.
• Thoroughly documents assumptions, methodology, validation and testing to facilitate peer reviews and compliance requirements.
• Monitors, understands and adopts emerging technology that can affect the application of scientific methodologies and/or quantitative analytical approaches to problem resolutions.
• Succinctly delivers complex analysis/findings in a manner that conveys understanding, influences senior executives, garners support for recommendations, drives business decisions, and influences business strategy.
• Provides subject matter expertise in operationalizing recommendations.
• Researches and pursues the latest and/or industry leading practices in data and analytics, (Ex: Cloud, Data Mining, Python, Neural Networks, Sensor data, IoT, Streaming/NRT data) to advance the overall DSA community knowledge.
• Identifies opportunities to continue to learn in the data and analytics space, whether informal (E.g., Coursera, Udemy, Kaggle, Code Up, etc) or formal (E.g. Certifications or advanced coursework).). 
• Innovates to create new and emerging data sets, and incorporation (data wrangling, data munging) into new insights

Minimum Requirements:

Minimum Education:

• Bachelor's degree in quantitative analytics field such as Economics, Finance, Statistics, Mathematics, Actuarial Sciences, Operations Research, Data and/or Business Analysis, Data Science or other quantitative discipline is required; OR 4 years of related statistics, mathematics or quantitative analytics or related experience (in addition to the minimum years of experience required) may be substituted in lieu of degree. (Total of 12 years without degree)

Minimum Experience:

• 8 years of data & analytics experience; OR 6 years of data & analytics experience and up to 2 years of progressive functional business relevant experience within the respective industry of responsibility (Life Insurance Industry) for a total of 8 years combined experience; OR Advanced degree in quantitative analytics field such as Economics, Finance, Statistics, Mathematics, Actuarial Sciences, Operations Research, Data and/or Business Analysis, Data Science or other quantitative discipline and 6 years of experience in data/analytics or functional business experience within the respective industry of responsibility (Life Insurance Industry).
• 3 years of direct and/or indirect management experience.
• Demonstrates expert skills in mathematical and statistical techniques and approaches used to drive fact-based decision-making.
• Expert knowledge of data analysis tools, data visualization, developing analysis queries and procedures in SQL, SAS, BI tools or other analysis software, and relevant industry data & methods and ability to connect external insights to business problems.

Preferred Experience:

• US military service or a military spouse
• 3 or more years of Marketing segmentation experience
• Working knowledge of Life Insurance, Annuity or Health products and services
• 2 or more years of working experience with Python or R
• 2 or more years of applied Machine Learning working experience
• Ability to lead cross functional teams
• Strong communication skills and ability to speak to executive leadership