Okay, I've searched for data science internship roles in San Francisco, CA within the ad-tech industry, requiring Python and SQL skills, and seeking candidates pursuing degrees in STEM fields. I have found 27 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Science Co-Op role at LiveRamp is fundamentally designed to immerse an aspiring data scientist in the practical, fast-paced environment of ad-tech, focusing on applying statistical and machine learning methods to real-world marketing data challenges. The candidate is expected to translate strong theoretical foundations in mathematics, statistics, and computer science into production-quality code and data models that measure and enhance ad performance. The role demands an ability to work with complex, large-scale datasets across multiple cloud platforms and big data technologies, such as Spark, BigQuery, AWS, and GCP. The responsibilities go beyond coding; they include prototyping novel modeling approaches, collaborating closely with cross-functional data science and engineering teams, and troubleshooting sophisticated models—activities that illustrate a high level of problem-solving autonomy and intellectual curiosity. Success in this role will not only be evidenced by delivering mission-critical projects within the internship period but also by demonstrating adaptability to a startup-like culture, a proactive attitude towards continuous learning, and strong teamwork skills in a dynamic environment. The role will likely confront challenges such as managing data quality across disparate sources, balancing exploratory R&D with production demands, and interpreting model assumptions accurately to inform business decisions. Being immersed in LiveRamp’s operational ecosystem also points to an implicit expectation to align technical deliverables with the broader mission of safe and effective data use in marketing technology.

Company Analysis:

LiveRamp operates as a premier data connectivity platform positioned at the nexus of data privacy, marketing effectiveness, and digital transformation. As an innovator and leader in the ad-tech and data collaboration space, LiveRamp balances rapid technological development with a commitment to responsible data use—creating an environment that is simultaneously cutting-edge and ethically conscious. Their culture values curiosity, humility, collaboration, and fun, which suggests a workplace that is intellectually rigorous but supportive and inclusive. For an intern or newcomer, this means the ability to thrive requires not only technical excellence but also a growth mindset and respectful communication. The company’s emphasis on mentorship, inclusive hiring, and cross-continental collaboration indicates a multi-layered organizational structure with opportunities for visibility and impact despite being an intern role. Positioned in San Francisco within a global network, the role sits at a strategic intersection where data science efforts directly feed into LiveRamp’s core mission to empower businesses to use data safely and effectively. This linkage underlines the importance of strategic alignment—the Co-Op role is a critical investment in talent development aimed at scaling future full-time hires who will sustain and accelerate LiveRamp’s growth and innovation trajectory.

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

• Behavioral: Can you talk about a time when you had to learn a complex technical skill quickly and apply it to a project? What approach did you take?
• Behavioral: Describe an experience where you worked on a team to solve a difficult problem. How did you manage differing opinions or conflicts?
• Behavioral: Tell me about a situation where you made a mistake in your analysis or code. How did you identify it and what steps did you take to correct it?
• Behavioral: How do you stay motivated and productive in a fast-paced, startup-like environment where priorities might shift rapidly?
• Technical: How would you design a statistical model to measure ad performance using large, disparate datasets? What metrics and data features would you prioritize?
• Technical: Explain your experience with SQL and PySpark. How have you used these tools to build data pipelines or conduct analysis?
• Technical: Describe how you would implement and troubleshoot a randomized control trial in an ad-tech setting. What are key assumptions you would verify?
• Technical: What steps do you take to transition a prototype machine learning model from R&D to a production environment?
• Situational: Imagine you notice a significant discrepancy between model predictions and actual ad performance metrics after deployment. How would you investigate and address this issue?
• Situational: You are tasked with designing an automated job to monitor data quality across multiple cloud platforms and alert the team of anomalies. How would you approach this challenge?
• Situational: Suppose an engineering teammate disagrees with your statistical approach during a project, citing scalability concerns. How would you handle this disagreement to reach a productive resolution?
• Company Fit: Why are you particularly interested in working at LiveRamp, and how do you see your work advancing our mission to make data use safe and effective?
• Company Fit: Given LiveRamp’s emphasis on curiosity and humility, how do you embody these values in your learning process or teamwork?
• Company Fit: How do you think LiveRamp’s focus on diversity, inclusion, and belonging might influence your experience and contributions here?
• Questions for Interviewer: Can you describe the typical career progression for interns who transition to full-time roles at LiveRamp?
• Questions for Interviewer: What are some current challenges the Insights Engineering team is facing, and how could I, as a Data Science Co-Op, contribute to addressing them?
• Questions for Interviewer: How does LiveRamp foster collaboration between data science, engineering, and product teams during fast-moving projects?