Okay, I've searched for Data Scientist positions in Tampa, FL that require a Bachelor's degree, include skills in Python and NLP, and are full-time roles. I have found 47 positions for you.

Sure. Here's the analysis:

Job Analysis:

This Data Scientist role at Shuvel Digital, working through Lensa, is fundamentally about creating innovative AI-driven data science solutions that extract actionable insights from complex and often unstructured data sources. The core purpose is to design and implement advanced analytical pipelines and microservices using a blend of machine learning techniques including NLP, anomaly detection, recommendation systems, and graph analytics. Success hinges on the ability to collaborate closely with subject matter experts, team leads, and vendors to define robust prototypes and transition them seamlessly into production environments. The position demands a balance of deep technical proficiency—particularly in Python, SQL, various data visualization tools (Tableau, Qlik), graph databases like Neo4J, and related query languages—and a strategic mindset to unify disparate bulk data into coherent, insightful analytics. Candidates are expected to navigate the ambiguity of unstructured data, adapt to feedback from both leadership and users, and deliver tangible outputs such as visualizations and documents. The role inherently involves cross-functional coordination which implies strong communication and interpersonal skills to manage expectations and refine solutions. Performance success likely includes delivering reliable, scalable AI pipelines and clear, actionable data products that empower analysts and decision-makers within the company, especially during the critical initial 6–12 months while transitioning prototypes into stable operational tools.

Company Analysis:

Lensa operates as a sophisticated career platform leveraging cutting-edge machine learning to revolutionize how job seekers connect with appropriate opportunities. Positioned as a market innovator that has already scaled to over 10 million users, Lensa combines technology with human-centric design, focusing on reducing friction in talent acquisition and enhancing the personalization of job searching. The company culture likely values agility, innovation, and a data-driven ethos given its roots in ML and the ongoing growth of its R&D teams spread across the U.S. and Europe. This environment suggests a fast-paced, collaborative setting where continuous learning and cross-team cooperation are crucial. For the Data Scientist role, this means working at the intersection of product innovation and real-world impact, within a company whose mission centers on improving people's career journeys. Given the team size and the technical nature of the work, the role probably occupies a highly visible, individual contributor position with close ties to leadership and product teams. Strategically, this hire supports Lensa’s goal of scaling its AI capabilities and enriching the platform's analytic offerings, implying a need for someone who can confidently bring prototypes into production and respond fluidly to evolving business challenges. Long-term success will depend on aligning technical ingenuity with a clear understanding of user and client needs, contributing to Lensa’s broader vision of transforming job search into a more intuitive, personalized experience.

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

• Behavioral: Tell me about a time when you had to collaborate with subject matter experts and vendors to develop a data science solution. How did you manage differing perspectives?
• Behavioral: Describe a challenging instance when you had to extract meaningful insights from unstructured text data. What approach did you take and what were the outcomes?
• Behavioral: Can you share an experience where you transitioned a data pipeline or prototype from development to production? What obstacles did you face and how did you overcome them?
• Behavioral: Give an example of when you received critical feedback on your work from leadership or users. How did you incorporate this feedback to improve your deliverable?
• Technical: How would you design and implement a microservice for anomaly detection using Python? Please outline your approach and tools you would utilize.
• Technical: What methods and tools have you used for graph traversal and network analytics? Could you explain how you would apply them using Neo4J or other graph databases?
• Technical: Explain your experience with NLP on unstructured data sets, particularly extracting entities and relationships. What frameworks or models have you found most effective?
• Technical: Describe your experience integrating disparate bulk data sources into a unified database schema. What challenges did you encounter?
• Situational: Imagine you are tasked with building a recommendation system for job seekers based on their skill profiles and past applications. How would you approach this project?
• Situational: You discover that a data visualization dashboard you created is not being adopted by analysts as intended. How do you handle this situation to improve the outcome?
• Situational: Suppose the production deployment of a custom data pipeline encounters intermittent failures and data inconsistencies. What steps would you take to diagnose and resolve the issue effectively?
• Company Fit: Why are you interested in working at Lensa, and how do you see your skills contributing to the company's mission to transform job search?
• Company Fit: Lensa emphasizes human-centric AI solutions. How do you ensure that your data science work prioritizes real user needs and ethical considerations?
• Company Fit: How do you stay motivated and productive in a fast-evolving tech environment like Lensa's, where continuous iteration and close collaboration are key?
• Questions to Ask Interviewer: How does the data science team currently collaborate with product and engineering teams when developing new AI features?
• Questions to Ask Interviewer: What are the biggest challenges Lensa faces in scaling its AI-driven matching algorithms, and how could my role contribute to overcoming these?
• Questions to Ask Interviewer: Can you describe the typical career progression for a data scientist within Lensa, and what growth opportunities are available?