Okay, I've searched for Data Science internship and co-op roles in San Francisco, CA that require skills in Python and SQL, focused in the ad-tech or data connectivity industries, and suitable for students pursuing STEM degrees. I have found 74 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Data Science Co-Op role at LiveRamp is fundamentally designed to immerse an aspiring data scientist in the practical application of advanced statistical and machine learning techniques within the ad-tech industry. This role is not just about coding or theoretical knowledge; it requires translating complex models—often involving randomized control trials and bias reduction—into scalable production-ready workflows that run on large cloud infrastructures like AWS and GCP. The candidate will face challenges such as dealing with noisy, fragmented data sources, ensuring data models are both statistically sound and efficient at scale, and collaborating cross-functionally with both data science and engineering teams. Success in this role hinges on rapid learning, technical agility across multiple platforms (SQL, PySpark, Python ecosystem), and the ability to prototype innovative approaches that directly influence ad performance measurement. Strong foundational knowledge in statistics and programming is vital to untangling the assumptions behind models and pushing improvements forward. Moreover, the ability to navigate ambiguity, communicate insights effectively, and contribute ethically to a fast-paced environment sets apart a high-performing Co-Op. The internship’s expectation to contribute production code by the first week also implies a steep learning curve balanced by structured mentorship, emphasizing hands-on, real-world impact. Performance will likely be evaluated based on the quality of code submitted, the effectiveness of the models developed, and how well the Co-Op integrates with the team to address LiveRamp’s mission of safe and effective data use.

Company Analysis:

LiveRamp operates as a leader in the data connectivity platform space with a strong focus on privacy-safe, people-based marketing—a position that places it at the intersection of cutting-edge data technology and stringent regulatory landscapes. As a well-established yet innovation-driven company, LiveRamp combines startup agility with enterprise-grade responsibility. This blend fosters a culture that values curiosity, humility, and collaboration, all while encouraging fun and creativity. For a Data Science Co-Op, this environment means access to high-impact projects that influence product and market outcomes directly, working alongside seasoned engineers and data scientists invested in mentorship and growth. The company's emphasis on diversity, inclusion, and belonging signals a supportive workplace where thriving means leveraging unique perspectives to solve hard problems. Organizationally, this Co-Op role is situated on the Insights Engineering team, indicating close collaboration with both data science and engineering units, likely with visibility into project architectures and leadership feedback loops. Strategically, this internship serves as a critical pipeline for talent acquisition, aimed at nurturing full-time hires who will sustain and amplify LiveRamp’s growth. Candidates who align with LiveRamp's mission to enable ethical, customer-centered data use—and who bring a proactive, startup mindset—will find an environment conducive to professional development and long-term impact.

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

• Behavioral: Tell me about a time when you had to learn a new technical skill or tool quickly to complete a project. How did you approach it and what was the outcome?
• Behavioral: Describe a situation where you worked collaboratively with others to solve a complex problem. What role did you play and how did the team succeed?
• Behavioral: Can you share an example of when you identified an error or flawed assumption in a data model? How did you address it?
• Behavioral: Talk about a time you managed ambiguity in a project. How did you prioritize tasks and make decisions with incomplete information?
• Technical: How would you design and implement a method to reduce selection bias in an ad-tech dataset with multiple confounding variables?
• Technical: Walk me through your experience with translating statistical prototypes into production SQL or PySpark workflows. What challenges did you face and how did you overcome them?
• Technical: Explain how you would use randomized control trials data to measure ad performance effectively. What statistical frameworks would you consider?
• Technical: Describe your experience working with cloud environments like AWS and GCP, particularly in big data processing contexts using tools like Spark or BigQuery.
• Situational: Imagine you deploy a machine learning model into production that suddenly underperforms. How would you investigate and resolve the issue?
• Situational: Suppose you are given a dataset with many missing values and inconsistencies from disparate sources. What steps would you take to clean and prepare this data for modeling?
• Situational: You notice a disagreement between data science and engineering teams about the assumptions behind a model. How would you facilitate resolution and ensure model reliability?
• Company Fit: Why are you interested in working at LiveRamp, and what about our mission to make data use safe and effective resonates with you?
• Company Fit: How do you think your personal values and work style align with LiveRamp’s emphasis on curiosity, humility, and collaboration?
• Company Fit: LiveRamp values diversity and inclusion as part of its culture. How have you contributed to or benefited from diverse and inclusive team environments in the past?
• Questions to Ask Interviewer: Can you describe how the Insights Engineering team collaborates with other departments and what the onboarding process looks like for a Co-Op?
• Questions to Ask Interviewer: What are some of the current biggest challenges LiveRamp is facing in the development of data models for ad performance?
• Questions to Ask Interviewer: How does LiveRamp support continuing education and growth for interns transitioning into full-time roles?