Senior Manager, Product Intelligence, AI/ML & Data Solutions

As the Senior Manager, Product Intelligence, AI/ML & Data Solutions at PENN Entertainment, your main responsibilities include developing a strategic roadmap for data-driven feature innovation, leading teams of engineers and analysts, collaborating across multiple departments, and overseeing the measurement and optimization of feature performance through A/B testing. You'll play a pivotal role in shaping user experiences powered by AI and data science, which will help drive business growth.

To qualify for the Senior Manager, Product Intelligence, AI/ML & Data Solutions role at PENN Entertainment, candidates should have at least 7 years of experience in delivering data science and analytics solutions. A Bachelor's degree in a technical or quantitative field is required, while a Master's degree is preferred. Experience with product analytics tools, a solid understanding of data science principles, and strong communication skills are crucial for this position.

The Senior Manager, Product Intelligence, AI/ML & Data Solutions plays a vital role in enhancing the user experience by developing data-driven features that personalize interactions and engage users effectively. This involves identifying emerging technologies, driving the ideation of new concepts, and leading initiatives that transform user insights into actionable developments within the app.

At PENN Entertainment, the work environment for the Senior Manager, Product Intelligence, AI/ML & Data Solutions is collaborative and engaging. The team is dedicated to innovation in digital gaming, and you'll work with cross-functional departments like engineering, user research, and product strategy. PENN fosters a culture of growth, encouraging employees to push boundaries and explore new opportunities, all while being part of a supportive team.

In the role of Senior Manager, Product Intelligence, AI/ML & Data Solutions at PENN Entertainment, you'll engage with a range of cutting-edge technologies that encompass data science, machine learning, and analytics tools. Familiarity with A/B testing methodologies, data modeling frameworks, and visualization best practices is essential, as these tools will be integral to measuring and optimizing the performance of the innovative features you develop.

When answering this question, provide specific examples of projects you've led, emphasizing your leadership role, the methodologies you employed, and the outcomes achieved. Highlight technologies used and how your leadership contributed to the project's success.

Discuss your approach to prioritization, including how you assess the impact, feasibility, and alignment with business objectives. Mention how you utilize user feedback, data insights, and collaboration with stakeholders to make informed decisions.

Provide a clear overview of the A/B testing processes you've implemented, including experimental design, metrics to measure success, and how you analyze results to drive product decisions. Mention any tools or frameworks you're experienced with.

Share your strategies for bridging gaps between technical and non-technical audiences. Highlight instances where you successfully translated complex data concepts into actionable insights for various stakeholders. Emphasize the importance of clarity and collaboration.

Discuss current and emerging trends you've noted in AI and data science, such as personalization, predictive analytics, or ethical AI. Provide examples of how these trends could enhance user experiences within gaming or media applications.

Use the STAR method (Situation, Task, Action, Result) to describe a specific challenge related to product development or team dynamics. Focus on your problem-solving skills and what you learned from the experience.

Explain your metrics and KPIs for evaluating feature performance. Elaborate on how you integrate user feedback, usage data, and A/B testing results into your measurement strategy to continuously refine and improve features.

Share methods you use to keep updated on industry trends, such as attending conferences, participating in webinars, subscribing to leading journals, or engaging with online communities. Mention any certifications or courses you've undertaken as well.

Provide a detailed account of a relevant project, focusing on your role in the ideation phase, the process of working with cross-functional teams, and the outcomes of the launch. Highlight any obstacles faced and how you navigated them.

Discuss your preferred data visualization tools, explaining their strengths and how you've used them in past roles to communicate data insights effectively. Provide examples of specific projects where visualization helped convey critical information.