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Data Scientist – Underwriting Innovation & Product Analytics (Remote)

    • Remote (United States)
  • Operations
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We exist to help people achieve financial clarity. At Thrivent, we believe money is a tool, not a goal. Driven by a higher purpose at our core, we are committed to providing financial advice, investments, insurance, banking and generosity programs to help people make the most of all they’ve been given.

At our heart, we are a membership-owned fraternal organization, as well as a holistic financial services organization, dedicated to serving the unique needs of our clients. We focus on their goals and priorities, guiding them toward financial choices that will help them live the life they want today—and tomorrow.

Job Summary

The Data Scientist – Underwriting Innovation & Product Analytics holds a key leadership position at Thrivent. You will be responsible for bringing transformational change to Thrivent by helping drive growth, delivering operational efficiencies and deriving impactful business insights. You will influence this change through your expertise in advanced analytics and your unique ability to communicate ideas with leadership and peers. Along with advanced data analysis and hypothesis testing, you will also be responsible for exploring and certifying innovative, new data sources and methodologies that have not previously been utilized at Thrivent. You will build predictive models and explore emerging AI technologies to creatively solve unique business challenges.

This role involves not only consulting with internal and external stakeholders to define analytic requirements and solutions, but also leading and educating others in advanced predictive analytics and data strategies. Exhibiting significant independence and strategic thinking, you will collaborate closely with business partners while providing mentorship and direction to junior team members. In addition to developing comprehensive knowledge of Thrivent’s products, systems, and processes, you will need to stay at the forefront of emerging technologies and industry best practices. The Data Scientist plays a pivotal role in shaping the data science strategy and driving data-informed decision making within the organization.

Life and health insurance underwriting is one the most exciting domains for advanced analytics. It is ripe with opportunities and will provide challenges worthy of your experience and expertise.

**We are open to candidates working remotely anywhere across the United States.

Job Responsibilities and Duties

  • Advanced Business Problem Analysis and Solution Development: Independently lead the analysis of complex business challenges, developing and proposing sophisticated data-driven solutions. This involves not just collaboration, but also steering projects and driving decision-making processes.
  • Comprehensive Data Collection and Preprocessing: Independently manage and optimize the collection and preparation of diverse data sources. Employ advanced data mining and preprocessing techniques, ensuring data quality and suitability for complex analysis.
  • In-Depth Exploratory Data Analysis (EDA): Perform advanced EDA to extract deep insights, using more sophisticated statistical methods and visualization techniques. Lead the narrative in translating these analyses into actionable business strategies.
  • Hypothesis Testing and Advanced Model Validation: Independently conduct and oversee complex hypothesis testing and model validation, utilizing a variety of techniques to ensure robustness and reliability of models.
  • Leading Predictive Modeling Efforts: Take a lead role in developing and implementing advanced predictive models. Apply cutting-edge machine learning algorithms to solve critical business problems, and mentor junior team members in these techniques.
  • Strategic Insights Generation and Reporting: Generate strategic insights that influence business decisions. Lead the preparation and presentation of detailed reports and analyses to stakeholders, showcasing the impact of data science on business outcomes.
  • Direct Stakeholder Engagement and Relationship Management: Take a proactive role in engaging with business stakeholders. Lead discussions, understand and manage expectations, and independently handle client relationships and project requirements.
  • Applied Critical Thinking in Business Context: Utilize critical thinking to not only understand but also challenge and refine business strategies. Lead the application of data science methodologies to drive innovative solutions.
  • Leadership in Learning and Skill Development: Stay at the forefront of emerging trends in data science, machine learning and regulatory requirements. Lead internal training sessions and knowledge-sharing initiatives to elevate the team's capabilities.
  • Ownership of Data Science Initiatives: Take ownership of significant data science projects within the company. Drive innovative strategies and solutions, showcasing leadership and a deep understanding of the company's goals and challenges.
  • Model Governance and Regulatory Compliance: Stay informed of the latest regulatory trends and governance practices in modeling, machine learning, and artificial intelligence. Apply this knowledge to ensure that all models are developed and maintained in compliance with relevant laws and industry standards, contributing to the organization’s adherence to best practices and ethical guidelines.

Job Qualifications


Experience & Education:

  • Bachelor’s degree in Data Science or a related quantitative field such as Actuarial Science, Statistics, Mathematics or Computer Science.
  • 3-5 years of relevant experience in data science or a closely related field. This experience should include hands-on work with data analysis, statistical modeling, machine learning, and delivering actionable insights from data.

Technical Skills

  • Advanced Programming: Proficiency in Python, with an emphasis on writing production-ready code and a solid understanding of code efficiency and scalability.
  • Data Manipulation Tools: Experience with data manipulation and analysis libraries (e.g., Pandas, NumPy in Python).
  • Data Architectures: Deep experience working with both structured and unstructured datasets.
  • Database Management: Strong skills in managing, processing, and analyzing large datasets. Advanced knowledge of SQL databases.
  • Data Preprocessing: Skills in cleaning and preparing data for analysis, including dealing with missing data, outliers, and data transformation.
  • Statistical Analysis and Machine Learning: Deep understanding of statistical methods and machine learning algorithms. Ability to develop, tune, and implement models independently.
  • Data Visualization: Expertise in creating insightful visualizations and interactive dashboards, using tools like Tableau, Power BI, or advanced libraries in Python like Matplotlib, Seaborn, Bokeh, plotly (Python).
  • Big Data Technologies: Experience with big data tools and frameworks like Spark or similar technologies.
  • Model Deployment and MLOps: Knowledge of model deployment, monitoring, and maintenance. Familiarity with MLOps practices and tools, such as Databricks, SnowFlake, SageMaker, Kubeflow, mlflow, etc.

Analytical Skills

  • Complex Problem-Solving: Ability to tackle complex data problems and devise effective solutions.
  • Critical Thinking: Skilled in evaluating data and analytics from multiple perspectives to derive the most value.
  • Hypothesis Testing: Strong skills in designing and executing robust tests for data models and hypotheses.
  • Research and Development: Capability to conduct research for innovative data solutions and apply findings to business problems.

Soft Skills

  • Effective Communication: Proficient in communicating complex data insights to both technical and non-technical stakeholders.
  • Collaboration and Teamwork: Ability to work collaboratively with cross-functional teams and lead project segments.
  • Leadership Qualities: Aptitude for mentoring junior team members and leading project initiatives.
  • Adaptability and Continuous Learning: Eagerness to stay updated with the latest data science trends and technologies and adapt to evolving business needs.
  • Time Management: Skills in managing time effectively, especially when handling multiple tasks or projects.


  • Life Underwriting / Life Product Domain Knowledge : Experience analyzing large data sets and building and deploying predictive models in support of life insurance underwriting and/or life insurance product development and product profitability.
  • Cross-Discipline Collaboration : Track record of strong collaboration across multiple disciplines, including with both data scientists and actuaries.
  • Project Management : Demonstrated project management skills, including proficiency in managing complex data science projects from conception to implementation.
  • Drive for Results : Demonstrated ability to work independently and a drive to learn and master new technologies and techniques.
  • Education : Master’s degree in data science or a related quantitative field, or relevant certifications in data science (e.g., AWS Certified Machine Learning, Databricks Certified Data Scientist Professional).

Additional Information

  • This position is a full-time remote opportunity.
  • If you are in the Minneapolis, MN or Appleton, WI location you would have access to our corporate offices in these areas. 


Pay Transparency


Thrivent’s long-term growth depends on attracting, rewarding, and retaining people who are committed to helping others thrive with purpose. We accomplish this by offering a wide variety of market competitive compensation programs to attract, reward, and retain top talent. The applicable salary or hourly wage range for this full-time role is $115,224.00 - $155,891.00 per year, which factors in various geographic regions. The base pay actually offered will be determined by a variety of factors including, but not limited to, location, relevant experience, skills, and knowledge, business needs, market demand, and other factors Thrivent deems important.


Thrivent is unique in our commitment to helping people to be wise with money and live balanced and generous lives. That extends to our benefits.


The following benefits may be offered: various bonuses (including, for example, annual or long-term incentives); medical, dental, and vision insurance; health savings account; flexible spending account; 401k; pension; life and accidental death and dismemberment insurance; disability insurance; supplemental protection insurance; 20 days of Paid Time Off each year; Sick and Safe Time; 10 paid company holidays; Volunteer Time Off; paid parental leave; EAP; well-being benefits, and other employee benefits. Eligibility for receipt of these benefits is subject to the applicable plan/policy documents. Thrivent’s plans/policies are subject to change at any time at Thrivent’s discretion.


Thrivent provides Equal Employment Opportunity (EEO) without regard to race, religion, color, sex, gender identity, sexual orientation, pregnancy, national origin, age, disability, marital status, citizenship status, military or veteran status, genetic information, or any other status protected by applicable local, state, or federal law. This policy applies to all employees and job applicants.

Thrivent is committed to providing reasonable accommodation to individuals with disabilities. If you need a reasonable accommodation, please let us know by sending an email to  or call 800-847-4836 and request Human Resources.


Data Scientist – Underwriting Innovation & Product Analytics (Remote)

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Our DEI Perspective

At Thrivent, we know our organization is strengthened when we have a diverse team that reflects our clients and builds on the diverse perspectives of others.

We believe that humanity thrives when people make the most of all they've been given, which includes our history, heritage, individual and collective experiences. Take a moment to pause with us, reflect on where Thrivent is at in our DEI journey and look forward to the future in anticipation of great things to come.

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