Okay, I've searched for full-time Data Scientist roles in Greater Birmingham, AL that require 2+ years of experience, proficiency in Python and SQL, and include 401(k) benefits. I found 53 relevant positions available for you.

Sure. Here's the analysis:

Job Analysis:

This Data Scientist role at Shipt is fundamentally about leveraging advanced data science and machine learning techniques to optimize the delivery process, ensuring that customers receive high-quality and timely orders. The core of the role involves building predictive models and experimenting with delivery promise algorithms within the availability engine, which directly impacts customer satisfaction and operational efficiency. Key responsibilities include the end-to-end development, deployment, and ongoing refinement of machine learning models using Python, SQL, and visualization tools like Tableau, integrated with an understanding of market dynamics such as shopper pay, delivery windows, and micro-economics. Success here means not only technical proficiency but also a strong grasp of experimentation design and causal inference to rigorously test hypotheses that drive business decisions. Given the collaborative environment of multiple data scientists, the candidate must excel in cross-functional problem-solving and communication to translate complex model outputs into actionable insights. The position requires a blend of advanced quantitative skills—ideally from a MS or PhD background in Statistics, Applied Math, Computer Science—and practical experience deploying models in a production-like environment. The role demands initiative, comfort with ambiguity, and the ability to balance optimization techniques against real-world constraints and business needs. Performance metrics are likely tied to the accuracy, efficiency, and impact of predictive models on delivery success metrics within the first year, alongside effective model experimentation and iteration.

Company Analysis:

Shipt operates at the intersection of retail technology and on-demand delivery, positioned as a growing disruptor within the personalized delivery marketplace, supported by the stability and resources of Target Corporation. Its expansive reach—serving 80% of U.S. households across over 5,000 cities—indicates a scale that demands continuous innovation and data-driven optimization. The company culture, as reflected in its employee-first messaging, focus on community, diversity, and growth, suggests a collaborative and mission-oriented workplace that values impact and personal connection. For the Data Scientist, this means an environment that welcomes innovation but also requires alignment with broader company values of reliability, high-quality service, and inclusivity. The hybrid work model indicates flexibility but with an expectation for team connectivity and innovation through collaboration. Structurally, this role is embedded within a team of data scientists, likely reporting to senior data science leadership, and working closely with product, engineering, and operations teams, highlighting the cross-functional nature of the position and its visibility in driving company-wide strategic initiatives. Strategically, the hire supports Shipt’s mission to refine and scale its delivery promise capabilities, enhancing competitive differentiation in a fast-paced market. This is a forward-looking role designed not just for maintenance but to spearhead improvements and innovations that fuel Shipt’s growth and customer trust.

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

• Behavioral: Tell me about a time when you had to design and implement an A/B test or experiment to solve a complex business problem. What approach did you take, and what was the outcome?
• Behavioral: Describe a situation where you had to collaborate with cross-functional teams that had differing priorities. How did you ensure your data science insights were effectively communicated and adopted?
• Behavioral: Give an example of when you faced ambiguity in a project. How did you navigate uncertainty and deliver a successful result?
• Behavioral: Share an experience where you had to balance model performance with business constraints or operational realities. How did you approach this trade-off?
• Technical: Walk me through your experience building and deploying a machine learning model end-to-end. What tools and processes did you use for development, testing, and deployment?
• Technical: How do you approach feature engineering when working on forecast or predictive models in the context of delivery logistics?
• Technical: Explain your understanding of causal inference and how you would apply it to evaluate the impact of a new delivery promise algorithm.
• Technical: What experience do you have with optimization frameworks in machine learning pipelines, especially relating to scheduling or resource allocation?
• Situational: Imagine our delivery model suddenly shows reduced accuracy and delivery delays increase. How would you investigate and address this issue?
• Situational: If you were tasked with improving shopper pay models to increase both shopper engagement and profitability, how would you start your analysis?
• Situational: Picture a scenario where you need to convince a skeptical product team to implement your machine learning solution. How do you build trust and alignment?
• Company Fit: What motivates you about Shipt’s mission to combine technology with personal touch in delivery services?
• Company Fit: How do you see your experience contributing to Shipt’s goal of optimizing delivery promises within a retail tech environment?
• Company Fit: Shipt emphasizes diversity and community impact. How do these values resonate with you personally and professionally?
• Questions for Interviewer: Can you describe how the data science team currently collaborates with engineering and operations to implement new models?
• Questions for Interviewer: What are the biggest challenges Shipt anticipates in delivery forecasting and optimization that this role will need to address?
• Questions for Interviewer: How does Shipt measure the success of data science initiatives, and what career growth opportunities exist within the data science team?