Okay, I've searched for full-time Data Scientist roles in Salt Lake City, UT, with a hybrid work mode, at least 6 years of experience, and skills in Python and SQL. I found 47 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

This Data Scientist role at the University of Utah's Health Sciences Strategy team is fundamentally about turning complex and diverse datasets into actionable insights that shape strategic decision-making for the health system. The role demands a blend of technical expertise—such as statistical modeling, machine learning, and data visualization—and a high degree of collaboration across multiple stakeholders including analysts, clinicians, and health system leaders. Candidates need to be comfortable bridging quantitative rigor with clear, accessible storytelling to drive organizational planning and problem-solving. The position requires adaptability to shifting priorities and the ability to juggle multiple projects simultaneously, reflecting the dynamic nature of healthcare strategy. Educational and experiential requisites, including advanced degrees and proficiency in languages like Python or R, underscore the complexity of the data and the expectation for technical sophistication. The candidate will regularly face ambiguous data challenges, requiring both independent judgment in analysis and team-oriented responsiveness to feedback. Success in the first year will likely be measured by the candidate’s ability to deliver reliable, insightful analyses that directly inform strategy, produce polished visualizations, and enhance collaborative processes—all while growing as an internal consultant and technical resource.

Company Analysis:

Lensa operates as a pioneering tech-driven career platform focused on applying machine learning to optimize talent acquisition and job matching. As a company, it emphasizes innovation, data science excellence, and practical applications of AI to solve real-world human problems, suggesting a culture that values curiosity, agility, and continuous learning. Although this Data Scientist role is embedded within the University of Utah Health Sciences environment and supported by Lensa's hiring platform, the company’s roots in cutting-edge AI and analytics reinforce the importance of technical proficiency and forward-thinking approaches in the role. The partnership context implies that candidates must be versatile and collaborative across institutional boundaries. The University of Utah itself presents a mission-oriented, inclusive culture focused on improving healthcare outcomes and educational access, which means the candidate should align with values of equity, service, and cross-disciplinary teamwork. The hybrid work model and the reporting structure—directly to senior leaders—suggest the role carries respectable visibility and accountability, demanding a strong balance of independence and collaborative influence. Strategically, this hire appears to serve as a growth- and impact-oriented investment in enhancing health system strategy through data science, highlighting the role’s importance in supporting ongoing transformational initiatives within a large and complex academic medical center.

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

• Behavioral: Tell me about a time when you had to clean and integrate multiple disparate data sources to solve a complex problem. How did you approach it, and what was the outcome?
• Behavioral: Describe a situation where you had to communicate technical findings to a non-technical audience. What strategies did you use to ensure understanding?
• Behavioral: Can you share an experience where shifting priorities forced you to quickly adapt your project plan? How did you manage competing deadlines and team expectations?
• Behavioral: Give an example of a time you received critical feedback on your analysis or coding work. How did you handle it and what did you learn?
• Technical: How would you approach building a patient segmentation model using healthcare data? What methods and data considerations would you prioritize?
• Technical: Explain your experience with predictive modeling and data mining techniques. Which algorithms do you find most effective in clinical or operational healthcare contexts?
• Technical: What tools or approaches do you use for creating interactive dashboards and visualizations that can communicate complex findings to diverse stakeholders?
• Technical: How do you ensure your code and data analyses are well-documented and reproducible, especially in a collaborative environment?
• Situational: Imagine you are confronted with an incomplete or poorly structured dataset critical for a major strategic initiative. What steps would you take to proceed effectively?
• Situational: If a clinician and a healthcare administrator have conflicting perspectives on what an analysis should prioritize, how would you reconcile and address their needs?
• Situational: Suppose a high-level leader requests rapid insights on a shifting health system metric with little prior notice. How would you balance speed and accuracy under such pressure?
• Company Fit: Why are you interested in contributing your data science skills within the University of Utah’s Health Sciences Strategy team?
• Company Fit: How do you see your personal values aligning with the University of Utah’s commitment to improving access and serving underrepresented populations?
• Company Fit: Lensa emphasizes innovation and machine learning-driven solutions. How do you stay current with emerging technologies in data science and apply them thoughtfully in your work?
• Questions for Interviewer: How does the Health Sciences Strategy team prioritize projects and balance long-term initiatives with immediate organizational needs?
• Questions for Interviewer: What would success look like for the Data Scientist role after the first 6 and 12 months on the job?
• Questions for Interviewer: How does collaboration typically work between the Data Scientist, clinicians, and other analysts across the Health Sciences division?