Associate Data Scientist

The Associate Data Scientist role at Thrivent is the cornerstone of a progressive data science career, emphasizing skill development with practical application and focuses on mastering data handling, exploratory analysis, predictive modeling, machine learning, hypothesis testing, and insights generation. This role will collaborate closely with more senior Data Scientists on the team, offering a unique opportunity to understand business needs and apply critical thinking in contributing to innovative and analytical solutions. Responsibilities range from participating in simple projects to providing key contributions to more complex initiatives, with an expectation to progressively attain independence in engaging with business stakeholders and advanced analyses. The Associate Data Scientist will not only strengthen their technical and analytical skills but also gain a deep understanding of Thrivent’s products, systems, and processes, staying updated with emerging technologies and methodologies. This entry-level role is designed as a journey of growth, that evolves from learning the basics to becoming a key player in driving data-informed decisions and innovative data science solutions at Thrivent.

• This role can be remote within the United States

DUTIES & RESPONSIBILITIES:

The Associate Data Scientist assists other Data Scientists in completing and applying critical thinking for simple projects (and associated documentation), as well as developing skills to tackle more complex projects and customer communication.

This role is expected to develop preliminary skills, problem solving, and techniques essential to Data Scientists. During this evolution/learning process, the Associate Data Scientist is expected to:

• Business Problem Analysis and Solution Development: Collaborate with senior data scientists to deeply understand business challenges' scope and complexities, laying the foundation for targeted data science work.
• Data Collection and Preprocessing: Systematically gather and prepare data from various Thrivent sources. Apply basic data mining skills to ensure data accuracy and readiness for analysis, involving tasks like cleaning, structuring, and enriching raw data.
• Exploratory Data Analysis (EDA): Conduct preliminary analyses to identify trends, patterns, and insights using statistical methods and visualization techniques, forming the basis for more advanced analytics.
• Hypothesis Testing and Model Validation: Engage in hypothesis-driven analysis and A/B testing frameworks. Test assumptions and validate model results, which are crucial for making informed data-driven decisions.
• Development of Predictive Modeling: In partnership with senior data scientists, build and implement basic predictive models using machine learning. Focus on applications that enhance customer experiences, revenue generation, marketing segmentation, and other business outcomes.
• Insights Generation and Reporting: Transform data findings into clear, actionable insights for business stakeholders. Develop reports and presentations to effectively communicate these insights.
• Progressive Stakeholder Engagement: Gradually increase participation in meetings and discussions with business stakeholders under the mentorship of more experienced data scientists. Develop skills in effective communication, need assessment, and client expectation management, moving toward independent stakeholder interaction.
• Critical Thinking: Consistently apply critical thinking to understand the broader business context. Actively contribute to innovative solution development under expert guidance.
• Continuous Learning and Skill Development: Maintain up-to-date knowledge of emerging technologies, methodologies, and best practices in data science, machine learning, and the industry. Proactively engage in learning opportunities for ongoing professional development.
• Contribution to Data Science Initiatives: Actively participate in wider data science projects and initiatives within the Thrivent, aiding in the development of innovative solutions and strategies.

QUALIFICATIONS & SKILLS:

Required:

Experience & Education:

• Bachelor’s degree in Data Science or a related quantitative field such as Statistics, Mathematics, or Computer Science.
• 0-2 years of experience in data science or related fields, which may include internships, academic projects, or part-time roles. This experience should demonstrate a foundation in data analysis techniques, basic statistical modeling, introductory machine learning concepts, and the application of these skills in real-world or academic projects to extract insights and understand data patterns.

Technical Skills

• Programming Languages: Foundational knowledge in python programming language, which is commonly used in data science at Thrivent.
• Data Manipulation Tools: Familiarity with data manipulation and analysis libraries (e.g., Pandas, NumPy in Python).
• Data Architectures: Experience working with both structured and unstructured datasets.
• Database Management: Basic knowledge of SQL for data querying and manipulation, working with database tools such as MySQL, Postgres, etc.
• Data Preprocessing: Skills in cleaning and preparing data for analysis, including dealing with missing data, outliers, and data transformation.
• Statistical Analysis and Machine Learning Basics: Solid grounding in statistical concepts (e.g., regression, distributions, statistical testing) and basic machine learning algorithms (like linear regression, random forests). Understanding their applications and limitations, suitable for fresh graduates with relevant coursework or projects.
• Data Visualization: Ability to create informative and interpretable data visualizations using tools like Matplotlib, Seaborn, Bokeh, plotly (Python).

Analytical Skills

• Problem-Solving: Ability to approach and solve problems logically and effectively.
• Exploratory Data Analysis: Skills in conducting EDA to identify patterns, anomalies, and insights from data.
• Critical Thinking: Capacity to think critically about data, ask the right questions, make appropriate connections, and apply the right methodologies.
• Hypothesis Testing: Understanding of how to construct and test hypotheses using statistical methods.
• Attention to Detail: Demonstrate ability to meticulously manage and analyze data, ensuring accuracy and reliability in every aspect of work, which is crucial for identifying subtle patterns, anomalies, and insights that can inform critical business decisions.

Soft Skills

• Communication: Strong verbal and written communication skills, including the ability to present complex findings to non-technical audiences.
• Collaboration: Ability to work effectively in a team, collaborating with other data scientists, analysts, and stakeholders.
• Learning Agility and Adaptability: Eagerness to learn new tools, technologies, and techniques in the rapidly evolving field of data science.
• Time Management: Skills in managing time effectively, especially when handling multiple tasks or projects.

Preferred:

• Domain Knowledge: Understanding of the financial services and insurance products that Thrivent operates in.
• Big Data Technologies: Familiarity with big data platforms like Spark. Familiarity with these technologies will help support analysis at scale.
• Version Control: Basic knowledge of version control systems like Git.
• Cloud Data and ML Platforms: Experience working on cloud platforms, such as Databricks, SnowFlake, SageMaker, Kubeflow, mlflow, etc.
• 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.