Staff Data Scientist

The Staff Data Scientist at Motive is critical in building models for credit risk and fraud functions related to the Motive Card. This role involves collaborating with various teams to create and implement sophisticated underwriting models while leveraging machine learning techniques on large datasets to enhance risk assessment processes.

To be a successful candidate for the Staff Data Scientist role at Motive, you should possess a Bachelor's degree or higher in a quantitative field, such as Computer Science, Math, or Statistics, along with 7+ years of experience in data science, particularly focused on credit risk. Strong expertise in machine learning techniques and statistical programming is essential to excel in this position.

As a Staff Data Scientist, you will greatly influence the Motive platform by enhancing data-driven decision-making through the development of fraud and credit risk models. Your contributions will help stabilize our technology lead in the connected fleet management sector while ensuring that our services remain effective and reliable.

Familiarity with data-oriented programming languages such as SQL, along with statistical programming tools like Python and R, is required for the Staff Data Scientist position at Motive. Experience with PySpark can also be a significant advantage, as it enables you to manage large datasets efficiently.

Candidates should have a comprehensive background in data science with particular expertise in credit risk and fraud modeling. The role requires hands-on experience in applying statistical and machine learning techniques on substantial datasets and deploying data-driven models end-to-end.

Motive fosters a dynamic work culture that promotes career development and growth for its employees. As a Staff Data Scientist, you'll have opportunities to take on leadership roles in projects, mentor others, and advance to senior technical roles, contributing to innovative projects across the company.

Motive is committed to creating a diverse and inclusive workplace, making it an ideal environment for any Staff Data Scientist. With its focus on innovation and the application of cutting-edge technology, you’ll find numerous opportunities to make a meaningful impact along with a strong emphasis on teamwork and collaboration.

When asked about your experience with machine learning techniques, provide specific examples of projects where you've successfully implemented algorithms. Discuss any frameworks you've used, the challenges faced, and how you measured success. This will showcase your hands-on experience and problem-solving skills.

For this question, share your methodology for building a credit risk model, including data gathering, feature selection, choosing the appropriate algorithm, and validating the model. Highlight your emphasis on understanding the business objectives throughout the process to align the model effectively with organizational goals.

Explain the nature of the dataset, the key challenges it presented, and what techniques you employed to analyze and derive insights. This will demonstrate your analytical capabilities and how you adapt to varying complexities in data.

Discuss specific non-traditional data sources you’ve worked with, how you incorporated them into your models, and the real-world impact they had on outcomes. This emphasizes your forward-thinking approach and willingness to explore innovative methods.

In answering this, detail the steps from model development to deployment, focusing on collaboration with teams and addressing any hurdles encountered. Mention the outcomes achieved post-deployment to illustrate your model's effectiveness.

Provide a list of programming languages you are skilled in, such as SQL, Python, or R. Share examples of how you’ve used these languages to solve problems or automate processes, showcasing your technical proficiency.

Mention specific resources such as blogs, webinars, or conferences you follow to keep abreast of industry trends. Highlight any communities or networks you engage with to show your commitment to continuous learning.

Illustrate your experience working in cross-functional teams, emphasizing how you communicate complex data concepts to non-technical stakeholders. Providing insights into your collaborative approach will demonstrate your interpersonal skills.

Discuss different performance metrics relevant to your work, such as accuracy, precision, recall, or AUC-ROC curves. Providing specific examples of how you’ve evaluated models will show your analytical rigor.

Share your passion for data science and the impact you believe it can have on organizations. Talk about your desire to solve complex problems and how this aligns with Motive's mission of improving operational efficiency through innovative data solutions.