Data Scientist

Data Scientist Objective

To support strategic corporate goals and initiatives via a wide range of descriptive, predictive, statistical, and advanced analytical techniques. To understand analytical needs from business areas and interpret them into technical approaches for analyses that result in improved business outcomes. To translate technical concepts and findings back into business context, assisting with, suggesting, and presenting analytically based recommendations. To extend analytical solutions into reproducible, automated pipelines. To provide technical support, expertise, collaboration, and mentorship to other analytical roles within the organization.

Data Scientist Responsibilities

Understand insurance principles, operations, and domains. Translate specific business needs into analytical concepts and approaches. Develop models and Data Analytics (DA) solutions that support various corporate business outcomes.

Develop and communicate requirements around the preparation of various sources and structures of data. Maintain a close, collaborative working relationship with the Data Analytics Engineer (DAE) role in the development of pipelines feeding DA solutions. Provide peer review of queries and scripts developed across the DA team.

Perform in-depth profiling and exploratory data analysis (EDA) to identify relationships, redundancies, correlations, and anomalies within data, modifying the dataset appropriately to account for any such findings. Carry forward surfaced insights into more advanced modeling phases. Enrich modeling datasets with additional features using a variety of feature engineering techniques.

Utilize a diverse set of data mining, machine learning, and advanced analytical techniques. Comfortably draw from a variety of both supervised and unsupervised learning algorithms, further researching and modifying such methods as warranted. Utilize a variety of techniques and diagnostic metrics to evaluate models. Adhere to CRISP-DM principles during analyses and modeling efforts. Utilize appropriate data visualization techniques and tools that explore, surface, and intuitively communicate meaningful business insights.

Investigate results of EDA, analyses, and models, evaluating for actionable business insights. Translate, present, and discuss relevant results with business stakeholders, including potential next steps for consideration. Craft communications that are targeted and intuitive to various stakeholders, levels of management, and committees. Effectively utilize a variety of communication channels including informal overviews, written summaries and reports, and formal presentations. When using visualizations to communicate insights or results, ensure that views are intuitive, well-understood, follow best practices, and are appropriately interactive to increase speed to insight and action.

Utilize and continue learning a variety of data-related tools, platforms, programming languages, and environments to build, test, simulate, and implement models. Partner with the DAE to develop end-to-end, reproducible, and automatable processes and pipelines for DA models and solutions, to be managed from within business areas where appropriate. Incorporate thorough unit and functional testing into DA solution development. Embed models with feedback mechanisms to help monitor for stability and continued suitability. Demonstrate urgency and diligence in assessing, correcting, documenting, and identifying permanent solutions to problems encountered in production settings.

Create and maintain thorough documentation of processes and procedures relating to DA initiatives and solutions, including problem identification, exploration and analysis of data, and development, testing, and deployment of solutions. Include conclusions drawn, next steps, and future analysis, maintenance, or monitoring required. Incorporate or cross-reference documentation of engineered datasets or pipelines that support the DA solution, for a holistic understanding of the full solution lifecycle.

Through the normal course of work, continually and creatively seek out, design, and recommend improvements to predictive modeling and analytical processes within DA solution pipelines. Evaluate opportunities for tooling and refactoring that improve efficiency, scalability, reusability, and automation.

Provide input to business stakeholders and DA management in support of strategic planning and roadmap development that promotes and drives adoption of predictive modeling and other DA solutions within the organization.

Assist with integration, user acceptance, and performance testing as needed. Partner with technical resources to productionalize and automate DA solutions where possible, following the organization’s version control processes in communication with the RM Team. Serve on related corporate projects if needed, providing support through all phases of solution implementation.

Partner with the DAE and other technical resources to become familiar with key sources of corporate data, their structure, related integrations or APIs, and lineage. Support the acquisition of new internal, external, and open data sources. Support and adhere to Data Governance (DG) guidance, best practices, and standards. Provide a direct line of sight around data defects and quality gaps, supplying feedback to DG or the relevant Data Owners/Stewards for awareness and remediation.

Actively pursue and assist business areas in enabling and automating functional processes using data and appropriate technology to improve efficiency and consistency within those processes.

Provide mentorship to other DA team members and other analytical roles. Help support, promote, and mature data literacy and fluency throughout the company and strive to make advanced analytical concepts more accessible to business partners and leadership.

Monitor industry methods, trends, tools, and use cases in the field of DA, machine learning, predictive modeling, and data science, especially as related to insurance. Research related topics as assigned or requested. Actively pursue related education and training via conferences, seminars, webinars, industry groups, publications, and other opportunities.

Required

Data Scientist Qualifications

Bachelor’s degree required, with Master’s degree preferred, preferably in data analytics, data science, mathematics, statistics, computer science, information systems, or other technical fields. Equivalent experience may be considered.

Minimum five years of experience as a data analyst, in an equivalent position, or working directly with data to analyze and solve complex business problems required. Other relevant work or internship experiences may be considered together with education attained.

Proficiency with various data structures, relational databases, machine learning tools/platforms, coding languages, and data visualization required.

Willingness to pursue continuing job-related training and education required.

Preferred

Experience with cloud environment tools/platforms strongly preferred.

Experience Working With Insurance Data Strongly Preferred.

Designations of Certified Specialist in Predictive Analytics (CSPA), Certified Business Intelligence Professional (CBIP), Associate in Insurance Data Analytics (AIDA), Certified Insurance Data Manager (CIDM), Certified Data Management Professional (CDMP), or related certification preferred.

Note This is a hybrid position working both remotely and in the Farm Bureau Home Office located in Lansing, Michigan.

Farm Bureau offers a full benefit package including medical, dental, vision, and 401K.