The Data Scientist role at Thrivent offers a significant advancement for data science 
professionals, combining deep technical expertise with a strong grasp of business applications. This pivotal position involves leading medium to complex data projects, utilizing advanced analytical techniques, predictive modeling, and machine learning algorithms to drive actionable business insights. You will also provide mentorship to junior team members and collaborate with senior colleagues on strategic projects. This role demands autonomy in data exploration, analysis, and solution implementation, blending technical skill with an understanding of the company’s business objectives. Your contributions will be crucial in driving data-informed decisions, influencing business strategies, and shaping the data science culture within Thrivent.
Key Responsibilities
Advanced Business Problem Analysis and Solution Development: Independently lead the analysis of complex business challenges and develop sophisticated data-driven solutions. Steer projects, drive decision-making processes, and collaborate effectively.
Comprehensive Data Collection and Preprocessing: Manage and optimize the collection and preparation of diverse data sources. Employ advanced data mining and preprocessing techniques to ensure data quality and suitability for complex analysis.
In-Depth Exploratory Data Analysis (EDA): Perform advanced EDA to extract deep insights using sophisticated statistical methods and visualization techniques. Translate these analyses into actionable business strategies.
Hypothesis Testing and Advanced Model Validation: Conduct and oversee complex hypothesis testing and model validation to ensure the robustness and reliability of models.
Leading Predictive Modeling Efforts: Develop and implement advanced predictive models using cutting-edge machine learning algorithms. Mentor junior team members in these techniques.
Strategic Insights Generation and Reporting: Generate insights that influence business decisions and prepare detailed reports and presentations for stakeholders, demonstrating the impact of data science on business outcomes.
Direct Stakeholder Engagement and Relationship Management: Proactively engage with business stakeholders, manage expectations, and independently handle client relationships and project requirements.
Applied Critical Thinking in Business Context: Apply critical thinking to challenge and refine business strategies, driving innovative solutions through data science methodologies.
Leadership in Learning and Skill Development: Stay updated with emerging trends in data science and machine learning. Lead internal training and knowledge-sharing initiatives to enhance the team’s capabilities.
Ownership of Data Science Initiatives: Take charge of significant data science projects, driving innovative strategies and solutions aligned with the company’s goals.
Model Governance and Regulatory Compliance: Ensure that all models comply with regulatory requirements and industry standards. Apply knowledge of regulatory trends and governance practices to uphold best practices and ethical guidelines.
Required Qualifications & Skills
Experience & Education:
Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field.
3-5 years of relevant experience in data science or a closely related field, including hands-on work with data analysis, statistical modeling, machine learning, and actionable insights.
Technical Skills:
Advanced Programming: Proficiency in Python for data science, including writing production-ready code and understanding code efficiency and scalability.
Data Manipulation Tools: Experience with libraries such as Pandas and NumPy in Python.
Data Architectures: Experience with both structured and unstructured datasets.
Database Management: Advanced skills in managing, processing, and analyzing large datasets. Proficiency in SQL.
Data Preprocessing: Skills in cleaning and preparing data, including handling missing data, outliers, and transformations.
Statistical Analysis and Machine Learning: Deep understanding of statistical methods and machine learning algorithms, with the ability to develop, tune, and implement models independently.
Data Visualization: Expertise in creating visualizations and interactive dashboards using tools like Tableau, Power BI, or Python libraries such as Matplotlib, Seaborn, Bokeh, and Plotly.
Big Data Technologies: Experience with big data tools and frameworks like Spark.
Model Deployment and MLOps: Knowledge of model deployment, monitoring, and maintenance, with familiarity in MLOps tools such as Databricks, Snowflake, SageMaker, Kubeflow, and MLflow.
Analytical Skills:
Complex Problem-Solving: Ability to tackle complex data problems and devise effective solutions.
Critical Thinking: Skilled in evaluating data from multiple perspectives to derive maximum 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: Proficiency in conveying complex data insights to both technical and non-technical stakeholders.
Collaboration and Teamwork: Ability to work 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 current with data science trends and adapt to evolving business needs.
Time Management: Skills in managing time effectively, especially with multiple tasks or projects.
Preferred Qualifications
Domain Knowledge: Understanding of financial services and insurance products.
Project Management: Basic skills in overseeing data projects from conception to delivery, including familiarity with Agile methodology.
Version Control: Proficiency with version control systems like Git.
Advanced Education: Master’s degree or PhD in a quantitative field.
NLP Experience: Experience in Natural Language Processing and Large Language Model technologies.
Compensation
Thrivent offers competitive compensation to attract and retain top talent. The salary range for this role is $115,224 – $155,891 per year, depending on factors such as location, experience, skills, and business needs.
Benefits
Thrivent is committed to supporting our employees’ well-being and offers a comprehensive benefits package, including:
401(k)
Vision Insurance
Disability Insurance
Medical, Dental, and Vision Insurance
Health Savings Account (HSA)
Flexible Spending Account (FSA)
Pension Plan
Life and Accidental Death & Dismemberment Insurance
Supplemental Protection Insurance
Paid Time Off (20 days annually)
Sick and Safe Time
10 Paid Company Holidays
Volunteer Time Off
Paid Parental Leave
Employee Assistance Program (EAP)
Well-being Benefits
Eligibility for benefits is subject to plan/policy documents and may change at Thrivent’s discretion.
Equal Employment Opportunity
Thrivent is an Equal Opportunity Employer and is committed to providing equal employment opportunities regardless of 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 law.
Reasonable Accommodation
Thrivent is committed to providing reasonable accommodations to individuals with disabilities. To request an accommodation, please email human.resources@thrivent.com or call 800-847-4836 and request Human Resources.
