Data Scientist, Mid-Level (Remote)

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Job Summary:

Thrivent is a company dedicated to helping individuals thrive with purpose, and they are seeking a Data Scientist to lead medium to complex data projects. This role requires advanced analytical techniques and machine learning algorithms to provide actionable insights, mentor junior colleagues, and influence business strategies.

Responsibilities:

• 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 integration, feature engineering, and pipeline design techniques to prepare high-quality data for modeling and analysis at scale, 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—including marketing attribution and optimization models—to solve critical business problems, particularly in areas like media mix modeling and channel effectiveness.

• 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.

Qualifications:

Required:

• Bachelor’s degree in Data Science or a related quantitative field such as Statistics, Mathematics, 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.

• Proficiency in data science languages, predominantly Python, with an emphasis on writing production-ready code and a solid understanding of code efficiency and scalability.

• Experience with data manipulation and analysis libraries (e.g., Spark, Pandas, NumPy in Python).

• Strong foundation in feature engineering practices: including building reusable data pipelines, orchestrating workflows, and integrating model-ready features in production environments. Deep experience working with both structured and unstructured datasets.

• Strong skills in managing, processing, and analyzing large datasets. Advanced knowledge of cloud, DataBricks, AWS, Snowflake, SQL databases.

• Skills in cleaning and preparing data for analysis, including dealing with missing data, outliers, and data transformation.

• Deep understanding of statistical methods and machine learning algorithms. Ability to develop, tune, and implement models independently. Experience with attribution modeling techniques such as Shapley Value, Markov chains, regression-based marketing mix modeling, or time-series-based response models.

• 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).

• Experience with big data tools and frameworks like Spark or similar technologies.

• Knowledge of model deployment, monitoring, and maintenance. Familiarity with MLOps practices and tools, such as Databricks, SnowFlake, SageMaker, Kubeflow, mlflow, etc.

• Ability to tackle complex data problems and devise effective solutions.

• Skilled in evaluating data and analytics from multiple perspectives to derive the most value.

• Strong skills in designing and executing robust tests for data models and hypotheses.

• Capability to conduct research for innovative data solutions and apply findings to business problems.

• Proficient in communicating complex data insights to both technical and non-technical stakeholders.

• Ability to work collaboratively with cross-functional teams and lead project segments.

• Aptitude for mentoring junior team members and leading project initiatives.

• Eagerness to stay updated with the latest data science trends and technologies and adapt to evolving business needs.

• Skills in managing time effectively, especially when handling multiple tasks or projects.

Preferred:

• Understanding of the financial services and insurance products that Thrivent operates in; experience with marketing analytics, channel attribution, or customer segmentation is a plus.

• Basic project management skills to oversee data projects from conception to delivery, leveraging frameworks such as Agile methodology.

• Proficiency in using version control systems, such as Git.

• Master’s degree or PhD in a quantitative field.

Company:

Thrivent is a financial services organization that helps Christians be wise with money and live generously. Founded in 1902, the company is headquartered in Minneapolis, Minnesota, USA, with a team of 5001-10000 employees. The company is currently Late Stage.