Senior Machine Learning Data Scientist

As a Senior Machine Learning Data Scientist at Extend, your primary responsibilities will include developing and deploying machine learning models aimed at detecting and preventing fraud, as well as enhancing decision-making processes. You will oversee the entire ML lifecycle, which includes everything from data preprocessing and feature engineering to model training, evaluation, validation, and deployment. Monitoring and maintaining these models in production to ensure optimal performance will also be crucial. Finally, collaboration is key; you’ll work closely with product and engineering teams to integrate your models into production applications.

To excel as a Senior Machine Learning Data Scientist at Extend, you should have a solid foundation in fundamental machine learning concepts and a strong grasp of software design best practices. Proficiency in Python is essential, especially with common ML libraries like Scikit-learn, Pytorch, or Tensorflow. Additionally, expertise in SQL is necessary, and familiarity with graph databases or large language models can be advantageous. Proven experience building fraud detection or risk assessment tools will definitely make your application stand out.

Extend prefers candidates with 3+ years of experience in building and deploying production machine learning models. Previous experience with fraud detection or risk assessment tools is a significant plus. Your qualifications should encompass a deep understanding of user behavior and fraud patterns, along with excellent communication and stakeholder management skills. A proactive approach to collaboration across multiple teams is essential for driving impactful results.

The Senior Machine Learning Data Scientist at Extend significantly contributes to our mission by developing models that enhance risk assessment and fraud detection capabilities. By leveraging advanced machine learning techniques and data analytics, you'll help streamline our post-purchase offerings, ultimately improving customer experiences and providing merchants with valuable insights. Your work not only bolsters the business's bottom line but also helps in creating trust and satisfaction among customers.

Extend fosters a collaborative and inclusive work environment for all team members, including our Senior Machine Learning Data Scientists. Working remotely, you'll be part of a diverse team that values learning, experimentation, and sharing best practices. With support from various departments, you’ll have the opportunity to grow your skills and influence within the company. Additionally, robust benefits like flexible paid time off, medical, dental, and vision coverage, along with competitive salaries, help to create a fulfilling workplace.

When answering this question, provide specific examples of projects you've worked on, detailing the data preprocessing steps you took, the models you chose, and the results achieved. Discuss the challenges you faced during deployment and how you overcame them. Emphasize your understanding of the complete ML lifecycle to show you can handle the requirements expected at Extend.

Discuss various techniques you’ve employed for feature engineering, such as transforming raw data into meaningful features that can significantly improve model performance. Mention specific examples related to fraud detection like creating new features to capture user behavior patterns, and explain how these features supported your models in producing accurate predictions.

Describe your approach to model validation, including techniques like cross-validation or A/B testing. Explain your methods for performance monitoring, such as using metrics like precision, recall, or F1-score, and detail how you've iterated on models in response to their performance over time. This shows your commitment to continuous improvement in a practical setting.

Share a specific situation where cross-departmental collaboration was crucial. Discuss the challenges encountered, like differing objectives or timelines, and emphasize your communication and negotiation strategies that helped align team goals and foster a collaborative spirit. This reflects your ability to work within Extend's team-oriented culture.

Discuss your experience with risk assessment specifically, including any models or tools you've developed to assess risks associated with fraud. Use specific metrics or case studies to illustrate how your work has reduced risks and added value through effective solutions, showcasing a quantifiable impact that aligns with Extend’s goals.

Focus on a particular instance where you had to adapt to new languages or technologies under a tight deadline. Outline your approach to quickly gaining proficiency, such as utilizing online resources or collaborating with team members, and discuss how successfully integrating this knowledge impacted your project outcomes positively.

Explain your understanding of bias in machine learning and the importance of ethical guidelines. Discuss methodologies you've applied to evaluate your models for fairness, transparency, and accountability, as well as any strategies you use to mitigate bias. Demonstrate your commitment to ethical practices, important for working at a socially-conscious company like Extend.

List the tools you frequently use, such as Jupyter Notebooks, Pandas, Matplotlib, or any visualization libraries that facilitate data exploration. Explain your rationale for using these tools in past projects, citing specific examples where visual data representation significantly influenced project decisions or stakeholder buy-in.

Detail your familiarity with AWS services relevant to machine learning, such as S3 for storage, EC2 for computing power, or SageMaker for building and deploying models. Share examples of how leveraging cloud environments has enhanced the scalability and efficiency of your projects, and emphasize how this experience aligns with Extend’s technological focus.

Convey your passion for the protection industry and your interest in how Extend is innovating in this space. Highlight specific elements from the company’s mission or recent projects that resonate with you, and convey your eagerness to contribute your skills while being a part of a company that prioritizes technology and customer experience.