Product Manager, Machine Learning (must be UK based)

As a Product Manager, Machine Learning at PolyAI, your primary responsibilities will include guiding the product roadmap for user-facing products, overseeing the launch of new features, and identifying opportunities for improvement within our machine learning systems. You'll work closely with various teams to ensure the successful integration of our voice assistants into client workflows, while also maintaining a keen understanding of market trends, evaluating the performance of ML models, and ensuring high-quality data collection.

To be considered for the Product Manager, Machine Learning position at PolyAI, the ideal candidate should have at least 2 years of relevant experience in product management or related fields, along with a strong background in machine learning. A Bachelor’s degree in Computer Science, Engineering, Statistics, or related disciplines is required. Additionally, experience with statistical systems and deep learning, as well as a proven track record of executing projects in fast-paced environments, will greatly enhance your candidacy.

At PolyAI, the Product Manager, Machine Learning plays a pivotal role in fostering a collaborative team culture. By leading cross-functional teams and inspiring engineers, data scientists, and other stakeholders, you'll create an environment where innovation and creativity thrive. Your interpersonal skills and ability to build strong relationships will be essential to driving engagement and motivating your team toward common goals.

The work environment at PolyAI is dynamic and vibrant, focused on innovation and collaboration. As a Product Manager, Machine Learning, you can expect a flexible working policy that allows for remote work and adaptability. Our culture encourages continuous learning and professional development, ensuring that you have access to a yearly learning budget and various resources to bolster your career growth.

The Product Manager, Machine Learning at PolyAI will enjoy numerous growth opportunities, including a dedicated learning budget, annual training sessions, and tailored professional development programs. Our commitment to continuous learning ensures that you can stay at the forefront of machine learning advancements and industry trends while advancing your career within our innovative company.

Certainly! In my previous roles, I have implemented machine learning models to improve product functionalities and user experience. Specifically, I focused on understanding user datasets to iterate on AI functionalities that enhance engagement. I ensure that my decisions are data-driven and based on rigorous statistical analysis, thus making a tangible impact on product outcomes.

I prioritize features based on a blend of customer feedback, market research, and alignment with strategic business goals. I use frameworks like the RICE scoring model to evaluate potential impact, effort, and reach, enabling me to make informed decisions that maximize value for both users and stakeholders.

In one project, we faced critical feedback during a beta testing phase for a new machine-learning feature. I led the team in conducting root cause analysis, which allowed us to identify the problem quickly. We created a list of prioritized tasks and iterated on our prototype based on user feedback, leading to a successful relaunch that improved user satisfaction significantly.

I prioritize open and regular communication by scheduling recurring check-ins and using collaborative tools like project management software. I create a culture of transparency by sharing updates and progress across teams. This ensures everyone is aligned and can contribute effectively to the project.

Key metrics I analyze include user engagement rates, customer satisfaction scores, and conversion rates post-launch. Additionally, I closely monitor performance metrics specific to machine learning models, like accuracy and precision, to understand the feature's effectiveness in serving user needs.

My approach includes conducting user interviews, surveys, and usability testing to collect qualitative and quantitative feedback. I also leverage A/B testing during beta phases to gauge user reactions to different options, helping me iterate on product features based on real user insights.

I facilitate discussions to understand each stakeholder's perspective and priorities. By utilizing a structured prioritization framework, I help guide decision-making discussions and ensure that stakeholder inputs align with broader organizational goals. This approach encourages collaboration and helps manage conflicts effectively.

I actively engage in continuous learning through industry publications, webinars, and international conferences focused on machine learning trends. Networking with fellow professionals in the field for knowledge sharing is also key, as is participating in relevant online courses to refine my skills and understanding.

I prioritize tasks based on urgency and importance, ensuring that critical elements are addressed first. I maintain close communication with my team and stakeholders to manage expectations while leveraging agile methodologies to break tasks into manageable sprints, promoting efficiency without compromising quality.

I thrive on the dynamic nature of technology, where innovation is continually shaping experiences. The opportunity to collaborate with diverse teams and drive changes that directly impact users' lives is incredibly fulfilling. I love the excitement of taking conceptual ideas through to realization and celebrating the successes that follow.