Okay, I've searched for Summer Data Scientist Associate roles in St Louis, MO that are full-time and remote, requiring proficiency in Python and SQL, and suitable for candidates pursuing a Master's or Ph.D. in Data Science or related fields. I have found 23 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

This Summer Data Scientist Associate role is fundamentally designed to cultivate emerging data science talent by embedding them within a seasoned team to solve real-world business challenges through data. The candidate is expected to take ownership of data-driven questions, contributing to the full modeling pipeline—from data extraction and transformation to feature engineering and visualization—and supporting the translation of complex technical insights into accessible business recommendations. The role’s requirements reflect the need for a technically solid foundation in Python and SQL, statistical methods, and machine learning algorithms to effectively contribute to model development and deployment within a real estate context. Since this is a summer associate role targeted at early-stage graduate students, the position demands both a solid grounding in analytical theory and practical experience, balanced with a genuine willingness to learn and adapt. Success in this role will likely be measured by the candidate’s ability to independently handle components of the data pipeline, collaborate fluidly within multi-disciplinary teams, and communicate findings to stakeholders with varying levels of technical expertise. The underlying challenges include navigating potentially ambiguous business problems, working with large, heterogeneous datasets, and ensuring model readiness for production in a complex IT environment. Candidates must demonstrate intellectual curiosity, strong coding proficiency, and an openness to innovation, enabling them to contribute fresh perspectives while integrating into the company’s established data science workflow.

Company Analysis:

Anywhere Real Estate is a dominant player in the global real estate market with a well-established portfolio of renowned brands. It operates at scale and leverages an integrated business model encompassing franchise, brokerage, relocation, title, and mortgage services, which sets the stage for complex data opportunities and impactful analytics. The company’s culture prioritizes innovation, empowerment, and collaboration, fostering an environment where strategic thinking and continuous learning are deeply valued – traits that align perfectly with the requirements of the Data Scientist Associate role. As an organization recognized for ethical standards and employee focus, Anywhere’s mission to ‘empower everyone’s next move’ not only guides its market activities but also permeates its internal culture, encouraging employees to be proactive contributors to growth and transformation. For a summer associate, this means working in a fast-paced, yet mission-driven setting where cross-functional collaboration and a 'people-first' mindset are essential. This role fits within a team-centric structure that likely interfaces directly with business units and IT infrastructure teams, offering visibility and the chance to influence production-level data initiatives. Strategically, this internship is designed not only as talent development but as a way to infuse fresh analytical capability into the company’s data ecosystem—helping to drive smarter decision-making in a highly competitive and evolving real estate industry.

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

• Behavioral: Tell me about a time when you had to clean and transform a complex dataset for analysis. What approach did you take and what challenges did you face?
• Behavioral: Describe an instance where you had to explain a technical concept to a non-technical stakeholder. How did you ensure they understood your message?
• Behavioral: Give an example of a project where you collaborated in a team setting. How did you handle differing opinions or approaches?
• Behavioral: Share a moment when you encountered ambiguity in a project. How did you manage your work to move forward effectively?
• Technical: How would you approach developing a predictive model using real estate transaction data? What key steps and considerations would you prioritize?
• Technical: Explain your experience with feature engineering and how you decide which features to create or transform.
• Technical: Describe your hands-on experience with Python libraries such as pandas, scikit-learn, or TensorFlow. Can you provide an example project where you applied these?
• Technical: What is your understanding of ETL processes, and why are they critical when working in production environments?
• Situational: Imagine you find inconsistencies within the dataset that affect model training results. How would you investigate and resolve them?
• Situational: If you were asked to build a model but found the business question unclear or too broad, what steps would you take to clarify and frame it effectively?
• Situational: Suppose your machine learning model performs well in testing but struggles during deployment due to IT constraints. How would you collaborate to address these challenges?
• Company Fit: Why are you interested in working at Anywhere Real Estate specifically, and what excites you most about applying data science within the real estate industry?
• Company Fit: How do your personal values align with Anywhere Real Estate’s commitment to innovation, empowerment, and ethical business practices?
• Company Fit: Given Anywhere’s integrated services model, how do you see data science driving value across multiple business units?
• Candidate Questions: What are some of the key data science projects currently underway at Anywhere that a summer associate might contribute to?
• Candidate Questions: How does the data science team at Anywhere collaborate with other departments, such as marketing, product, and IT?
• Candidate Questions: What opportunities exist for summer associates to receive mentorship and professional development during their internship?