Okay, I've searched for Data Scientist roles in New York requiring experience in Python and SQL, specifically in the advertising industry, for full-time positions, with a salary expectation of at least $125,500. I found 45 relevant positions for you.

Sure. Here's the analysis:

Job Analysis:

The role of Data Scientist for the Media Planning at Amazon Ads fundamentally revolves around leveraging data to enhance advertising products that drive business growth. This involves a complex interplay of analytics, machine learning, and collaboration with various stakeholders to align data science initiatives with the company's strategic objectives. The primary responsibilities include crafting the data science roadmap, identifying opportunities for impactful data-driven solutions, and employing advanced methodologies to solve real-world business problems. Success in this role will likely be reflected in the precision of the insights generated, the effectiveness of proposed models, and their subsequent impact on advertising strategies. Candidates must be prepared to handle myriad data types and sources, navigate cross-functional environments, and distill complex findings into actionable plans tailored for diverse audiences. This requires not only technical proficiency but also an understanding of the business implications of data insights, informing both strategic decision-making and tactical executions.

Company Analysis:

Amazon Advertising occupies a unique space in the rapidly evolving digital advertising sector, driven by data and innovation to enhance user experience and customer engagement. As one of Amazon's high-growth areas, the company emphasizes a culture of data-driven decision-making and agility, reflecting a modern, fast-paced, and results-oriented environment. Employees in such a setting can expect to collaborate across teams, potentially influencing critical advertising strategies at scale. The culture values inclusivity and encourages individuals to share insights, suggesting a need for strong communication skills and adaptability. The Data Scientist role directly aligns with Amazon's broader objectives of providing targeted, efficient advertising solutions. Given the context of an ambitious growth trajectory, this position is likely designed to proactively innovate and respond to emerging market needs, demanding both technical expertise and the ability to anticipate business trends.

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

• Tell me about a time when you had to use data to solve a complex problem. What approach did you take?
• Describe an experience where you had to communicate a technical concept to a non-technical audience. How did you ensure they understood?
• Can you provide an example of a project where you identified an outlier? How did you approach the situation?
• What frameworks or methods do you utilize to ensure data accuracy before making business recommendations?
• How would you approach developing a machine learning model for a new advertising product at Amazon?
• Explain a time you had to collaborate with cross-functional teams (like engineers or product managers) to achieve a common goal. What challenges did you encounter?
• Given the rapid changes in the advertising space, how would you prioritize your data science initiatives? What factors would influence your decisions?
• Imagine you are tasked with generating insights from a large dataset. What steps would you take from data extraction to presenting findings?
• Why are you interested in the Data Scientist role at Amazon Advertising specifically?
• In what ways do your values align with Amazon's commitment to innovation and customer focus?
• What do you think are the biggest challenges currently facing the digital advertising industry, and how can data science address them?
• How do you keep your data science skills and knowledge up to date given the fast-paced nature of technology?
• What questions do you have about the collaborations between the Data Science team and other departments within Amazon Advertising?
• How do you measure the success of a data-driven initiative or campaign? What key performance indicators do you consider most important?
• Could you elaborate on your experience with outlier detection and how you define statistical assumptions when analyzing data?