I found several Lead Machine Learning Engineer roles in Newport News, VA that meet your criteria for part-time positions. They require at least 6 years of experience and proficiency in Python, and some offer remote work options. You can explore 12 suitable positions now.

Sure. Here's the analysis:

Job Analysis:

The Lead Machine Learning Engineer at Capital One is fundamentally hired to integrate and optimize machine learning applications that directly impact the financial well-being of customers. This role requires a blend of technical acuity and collaborative spirit as this expert will work within an Agile framework, directly influencing the development and performance of ML systems. Responsibilities such as designing and building ML models, collaborating with Product and Data Science teams, and ensuring system robustness indicate that a successful candidate must not only possess technical expertise but also have the ability to translate complex technical language into actionable insights for non-technical stakeholders. Anticipating challenges like maintaining model accuracy in production and navigating inter-departmental dependencies is crucial. Therefore, success can be defined by the quality of ML solutions delivered, the efficiency of deploying these solutions, and the strength of cross-functional relationships established in the process.

Company Analysis:

Capital One operates at the intersection of banking and technology, positioning itself as an innovation-driven player in the financial services industry. With a commitment to customer empowerment and financial equity, the company fosters a dynamic culture that values collaboration and innovative thinking. This culture likely places an emphasis on agility, which aligns with the collaborative nature of the MLE role, augmenting the importance of adaptability and cross-functional communication. In the organizational context, the Lead Machine Learning Engineer will likely have significant visibility among senior leadership due to the strategic value of ML applications in driving customer-centric solutions. From a strategic standpoint, hiring for this role signifies Capital One's intent to scale its capabilities in AI-driven financial solutions, thereby aligning the engineering efforts directly with the overarching goals of innovation and enhanced customer experience.

Absolutely. Here are some mock interview questions that could come up:

• Tell me about a time when you had to collaborate with multiple teams to execute a machine learning project successfully. What challenges did you face, and how did you overcome them?
• Can you describe a particularly complex ML model you developed? What considerations did you take into account in terms of data selection and model tuning?
• Describe a situation where you identified a significant performance issue in a production ML model. What steps did you take to diagnose and resolve the issue?
• Explain your experience with CI/CD practices in deploying machine learning models. What best practices do you follow to ensure code quality and reliability?
• How do you ensure that your ML models are explainable and free from bias? Can you provide an example of a specific project where these considerations were vital?
• What is your experience using cloud platforms for machine learning solutions? Can you discuss a specific instance where you leveraged cloud services to enhance model performance?
• Imagine you receive feedback that a model you deployed is underperforming. What is your approach to diagnosing the problem and implementing a solution?
• Suppose you are tasked with designing a new ML model to enhance customer experiences. What key factors would you consider during the design phase?
• What attracts you to Capital One specifically, and how do you think your background aligns with our mission of empowering customers?
• How do you view the intersection of financial services and technology? What innovations do you anticipate will shape the future in this space?
• What do you think makes for a successful Agile team, especially in the context of machine learning engineering?
• Can you tell us about a project where you demonstrated leadership in developing ML practices? What insights did you gain from that experience?