Data Science Executive

As a Data Science Executive at System One, your primary responsibilities will include leading various analytical projects specifically for credit card decision science initiatives, supervising junior analysts, developing and executing machine learning models for credit decisioning, and analyzing large-scale credit card portfolio performance.

To qualify for the Data Science Executive position at System One, candidates must hold a Master’s degree in Statistics, Mathematics, Computer Science, or a related field. Additionally, experience in consumer lending data analytics and proficiency in programming languages like Python or PySpark are crucial.

A Data Science Executive at System One should be skilled in Python or PySpark for large-scale data analysis, comfortable using data visualization tools such as Tableau, and knowledgeable about machine learning model development and statistical analysis methodologies.

The Data Science Executive role at System One features a hybrid work schedule, combining both on-site and potentially remote work. Candidates must be local or willing to relocate within commuting distance of Charlotte, MI.

To apply for the Data Science Executive position at System One, please connect on LinkedIn and email your resume along with your work authorization status, current location, availability, and compensation expectations to denis.potapenko@systemone.com, ensuring to include the exact job title and location in your message.

When answering this question, share specific examples of machine learning models you've developed, the frameworks you used, the outcomes of those models, and how they contributed to your previous organization's objectives.

Discuss a specific project that posed challenges, how you approached it, the techniques you employed, and the successful result that came out of your hard work and analysis.

Explain your method for managing priorities, emphasizing effective communication, setting clear goals, and how you ensure team members feel supported while meeting deadlines.

Share your experience with various data visualization tools, particularly Tableau, and provide examples of how visualizations have enhanced stakeholder understanding and decision-making based on your analyses.

Discuss your systematic approach to credit risk assessment, highlighting any specific methodologies you rely on and how they inform decision-making in your projects.

Talk about the programming languages and tools you utilize for large-scale data analysis, especially in relation to Python or PySpark, and provide examples of datasets you've worked with and insights uncovered.

Mention the resources you rely on, such as blogs, seminars, workshops, or online courses, to stay informed about the latest trends in data science and how you apply this new knowledge in your work.

Present a specific instance where your data analysis led to actionable insights, detailing the decision-making process, implementation, and its overall impact on the organization.

Highlight your communication skills and approach to simplification. Provide examples of past experiences where you've successfully conveyed complex data insights to stakeholders with varied backgrounds.

Explain your mentoring philosophy, including techniques you use to guide junior analysts in developing their skills, fostering their growth, and ensuring they contribute effectively to team projects.