Okay, I've searched for Senior Data Scientist roles in Cincinnati, OH and Seattle, WA, requiring 8+ years of experience, proficiency in Python and SQL, within the facility maintenance or logistics industry, for full-time positions. I have found 142 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Senior Data Scientist role at DMG is fundamentally designed to embed advanced analytics within the heart of the company’s evolving Marketplace Health and Pricing teams. The primary purpose is to leverage data science to optimize pricing strategies and matchmaking functions in facility maintenance services—a domain that inherently involves complex operational logistics and large-scale data variability. Beyond technical modeling, the role entails partnering with diverse stakeholders to translate nuanced business challenges into actionable, scalable data products. This requires a blend of deep technical expertise—particularly in Python, SQL, cloud-native AWS environments, and large-scale data wrangling—and strong communication skills to bridge the gap between technical and business audiences. Given the entrepreneurial, fast-paced team environment, the candidate must excel at rapid prototyping, iterative product development, and collaborative problem solving under pressure, while maintaining high software engineering standards including version control, CI/CD, and containerization. Success in this role will be defined by the ability to deliver robust, production-ready analytics solutions that directly impact and elevate key business functions such as pricing accuracy, marketplace efficiency, and customer satisfaction within the maintenance services sector.

Company Analysis:

Divisions Maintenance Group (DMG) operates within the essential services market, providing facility maintenance solutions for multiple site commercial and industrial clients nationwide. Positioned as a stable yet innovation-oriented partner, DMG balances a customer-first and service-driven reputation with an ambitious push towards digital transformation through data science. The company emphasizes an inclusive, entrepreneurial culture that supports career growth and innovation. For someone in the Senior Data Scientist role, this implies a work environment that rewards initiative, adaptability, and forward thinking within a collaborative framework. The organization’s strategic commitment to building out Marketplace Health and Pricing analytics capabilities signals a transformative phase, where the role will be influential not only as a technical expert but as a strategic contributor advancing DMG’s competitive differentiation. Embedded in the Product & Engineering department and reporting to the Director of Engineering, this role offers considerable visibility and cross-functional interaction, enabling the individual to shape technical direction while navigating real-world operational complexities. The company's robust benefits and 'Great Place to Work' certification reinforce a supportive environment, aligning well with candidates who seek a blend of challenge, stability, and room to innovate in a mission-critical industry context.

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

• Behavioral: Tell me about a time when you had to translate a complex business problem into a data science solution. How did you ensure stakeholder alignment throughout the process?
• Behavioral: Describe an experience where you had to communicate technical findings to a non-technical audience. What approach did you take to make the concepts clear and actionable?
• Behavioral: Give an example of a project where you had to manage tight deadlines in a fast-paced environment. How did you maintain quality while meeting those demands?
• Behavioral: Can you share a situation where you identified a gap in data quality or availability and how you addressed it to ensure successful model development?
• Technical: How do you approach building and maintaining production-level analytics code, and what best practices do you follow to ensure robustness and scalability?
• Technical: Explain your experience with various machine learning techniques such as supervised learning, clustering, and optimization within a production environment. How do you decide which method to apply?
• Technical: Walk me through your experience with cloud-native tools (particularly AWS) and how you have used them to deploy and monitor data science models.
• Technical: How have you integrated version control and CI/CD pipelines in your data science projects to enable agile delivery and collaboration?
• Situational: Imagine a key stakeholder disagrees with the outcome of your predictive model. How would you handle their concerns and win their confidence?
• Situational: You are working with a large, unstructured data set that reveals unexpected patterns. How would you validate these findings and explore their business implications?
• Situational: Suppose you are tasked with rapidly prototyping a new pricing model with limited historical data. What steps would you take to develop, test, and deploy it effectively?
• Company Fit: What interests you most about working in the facility maintenance industry with DMG and contributing to the Marketplace Health and Pricing teams?
• Company Fit: How do you see your experience and values aligning with DMG's entrepreneurial spirit and commitment to inclusive culture?
• Company Fit: Given DMG's focus on uninterrupted peace of mind for its customers, how would you ensure your analytics solutions directly support this mission?
• Questions for Interviewer: Can you describe how the Marketplace Health and Pricing teams currently interact with other departments and what the main challenges have been?
• Questions for Interviewer: What are the key milestones or success metrics you expect the Senior Data Scientist to achieve in the first 6 to 12 months?
• Questions for Interviewer: How does DMG foster professional growth and continuous learning within the Product & Engineering teams?