Data Scientist II - CX Analytics

Unum•Portland, ME

2d•$73,300 - $150,500•Onsite

About The Position

We are seeking a Data Scientist II to join the CX Analytics team within our Customer Experience Organization. Our team leverages AI across development workflows and uses it to extract insights from both structured data and unstructured sources such as raw text, and you will apply these capabilities in your day-to-day work. In this role, you will focus on executing applied data science work—running analyses, supporting experiments, and mining data to uncover drivers and patterns that inform how the business supports its customers. You will work across structured and unstructured data to answer complex business questions and contribute to larger analytical efforts that connect findings to outcomes across BI/reporting, operations, and value stream partners. You will operate with a solid degree of independence on moderately complex work, partnering closely with BI Analysts and Data Engineers to ensure analyses are grounded in reliable data and effectively operationalized. The ideal candidate is a strong analytical thinker and collaborator who can dig into data, apply the right techniques to the problem at hand, and consistently turn analysis into meaningful insights that support better decision-making across the organization. This is a campus-based role. Current remote and field-based Unum employees may apply and will be considered in accordance with company policy.

Requirements

Bachelor’s degree in quantitative field is required, Master’s is preferred
4 years of professional experience or equivalent relevant work experience preferred
Deep expertise in at least two of the following skillsets preferred; and competency in the other: Programming & Process automation: Experience with file I/O, database integrations, and APIs to build automated analytics pipelines. Understanding of process research and design, which may be demonstrated through use of DevOps, automation, data mining, web scraping, or object-oriented software is preferred.
Data Visualization: Expertise on at least one visualization tool with working knowledge of others and expertise in static data visualization. Understanding of dynamic data visualization.
Statistics & Statistical modeling: Expertise using statistical inference and regression. Solid understanding of machine learning algorithms. Solid understanding of feature selection and extraction. Conducts end-to-end machine learning tasks from problem synthesis to model deployment.
Data Extraction, Transformation, and Loading: Preferred skills include expertise in writing complex SQL queries that join multiple tables/databases. Independently explore databases/tables to identify best data sources to solve business problems. Demonstrates ability to troubleshoot complex SQL queries with little guidance. Demonstrates ability to create logical data models by combining data from multiple sources including internal and external data.
Demonstrated communication skills, experience in financial services, leadership experience working with senior management and executive leadership, and attention to detail while effectively and independently prioritizing work and managing multiple projects simultaneously.
Demonstrated ability to coach or mentor team members, ability to commit quickly and positively to change. Viewed as a promoter of change management and leads proof of concept work and prototyping when necessary

Nice To Haves

Entrepreneurial self-starter, a thorough, results-oriented problem-solver, and a lifelong learner with voracious curiosity AND intermediate understanding of their organization

Responsibilities

Design and execute analytical solutions using optimization, simulation, data mining and other statistical methods with a focus on delivering actionable business value.
Integrate large volume of data from different sources (including DB2, SQL Server, Web API and Teradata) to create data assets for ad-hoc analyses and larger studies.
Apply validation, aggregation, and reconciliation techniques to create rich modeling-ready data framework.
Construct and tune predictive models to explain and understand observed events, forecast expected behavior, or identify risk through scoring or clustering.
Efficiently interpret results and communicate findings and potential value to influence manager and leadership decision making.
Support integration of solutions within existing business processes using automation techniques.
Understand theory and application of current and emerging statistical methods and tools.
Provides support, training and/or mentorship to lower-level Data Science peers.
Perform other related duties as assigned

Benefits

Award-winning culture
Inclusion and diversity as a priority
Performance Based Incentive Plans
Competitive benefits package that includes: Health, Vision, Dental, Short & Long-Term Disability
Generous PTO (including paid time to volunteer!)
Up to 9.5% 401(k) employer contribution
Mental health support
Career advancement opportunities
Student loan repayment options
Tuition reimbursement
Flexible work environments