Role Overview / Project Description: As a Technical Product Manager CW on the Recommendations Data Augmentation & Services Team, with extensive experience with recommendations systems, you will play a crucial role in ensuring the success of our online recommendations. You will leverage your unique balance of business, product, and deep technical skills to drive the development and enhancement of datasets and machine learning models. You will be responsible for owning and driving the roadmap, which encompasses the creation of high-quality recommendation services that provide accurate and inspiring product suggestions to our customers. Additionally, you will work closely with product/business partners, data engineers, data scientists, ML (Machine Learning) scientists, ML engineers, cloud infrastructure teams, and other stakeholders to prioritize and bring meaningful recommendations to life.

Position Summary/Job Description:

Key Responsibilities:

1. Develop and manage a visionary and comprehensive product roadmap for recommendations data augmentation and services.
2. Align product strategies with business goals and customer needs.
3. Collaborate with cross-functional teams to define and refine the roadmap for the Recommendations Data Augmentation & Services team.
4. Lead the entire product lifecycle in close partnership with engineering, from concept to launch.
5. Prioritize product features and improvements based on user feedback, data analysis, and market trends.
6. Drive the improvement of datasets and machine learning models to enhance recommendation accuracy. Create and improve recommendation services using these datasets and models.
7. Collaborate with cross-functional teams for seamless integration and delivery of recommendation services.
8. Build and maintain relationships with internal teams, positioning recommendations as their source of truth for product recommendations, with a focus on growing internal usage quarter-over-quarter.
9. Utilize data-driven insights to understand user behavior and preferences.
10. Collaborate with data scientists and analysts to optimize recommendation algorithms.
11. Focus on enhancing the user experience by delivering personalized and relevant recommendations.
12. Conduct A/B testing and user research to optimize recommendation data.
13. Monitor and analyze performance metrics to continuously refine and optimize recommendation algorithms.
14. Identify opportunities for improvement and implement strategies to enhance data performance and user engagement.
15. Stay updated with industry trends and advancements in machine learning and data science.
16. Communicate product updates, and roadmap effectively with peers and stakeholders.

REQUIRED Skills Overview:

1. Product Roadmap Development and Management: The ability to create and manage a comprehensive, visionary, and outward-looking product roadmap is crucial. This includes aligning product strategies with business goals and customer needs. Achieved by bringing a product mindset by first understanding the problems to solve and then working to define solutions.
2. Technical Expertise in Data and Machine Learning: Experience with recommendations systems is crucial to be successful in this role. A strong technical background in data augmentation, machine learning models, and recommendation systems is essential. This includes the ability to drive the improvement of datasets and collaborate with data scientists and engineers.
3. Cross-functional Collaboration and Leadership: Excellent collaboration skills to work effectively with cross-functional teams, including engineering, data science, and business partners. This also involves building and maintaining relationships with internal teams to position recommendations as their source of truth. The candidate must be able to lead a product area that has a strong group of engineers, listen to feedback, and make decisive decisions, including saying no when necessary.
4. Analytical and Data-driven Decision Making: Critical thinking and strong analytical skills to utilize data-driven insights for understanding user behavior, optimizing recommendation algorithms, and continuously refining product features based on performance metrics.
5. Project Management and Execution: Effective project management skills to lead the entire product lifecycle from concept to launch. This includes prioritizing features, managing timelines, and ensuring high-quality deliverables.

These skills will help ensure the candidate can successfully drive the development and enhancement of recommendation services, leading to a better user experience and business success. Coupled with

a range of 5 to 8 years of experience. This range ensures the candidate has sufficient expertise in product management, technical skills in data and machine learning, and the ability to lead and collaborate effectively with cross-functional teams.

Here are some nice-to-have skills that can boost a candidate s effectiveness in this role. While not strictly required, these additional skills can help a candidate excel and contribute to the overall success of the recommendations team.

1. Experience with Cloud Platforms: Familiarity with cloud infrastructure and services (e.g., AWS (Amazon Web Services), Azure, Google Cloud) can be beneficial for managing data and machine learning workflows.
2. Knowledge of Big Data Technologies: Experience with big data tools and technologies (e.g., Hadoop, Spark, Kafka) can help in handling large datasets and optimizing data processing.
3. User Experience (UX) Design: Understanding UX principles and having experience in UX design can help in creating more user-friendly and engaging recommendation interfaces.
4. Agile Methodologies: Experience working in Agile environments and familiarity with Agile tools (e.g., Jira, Trello) can improve project management and team collaboration.
5. Advanced Statistical Analysis: Proficiency in advanced statistical methods and tools (e.g., R, SAS) can enhance the ability to analyze data and derive meaningful insights.
6. Programming Skills: Knowledge of programming languages such as Python, Java, or Scala can be useful for prototyping and understanding the technical aspects of machine learning models.
7. Industry-Specific Knowledge: Experience in the e-commerce or retail industry can provide valuable context and insights into customer behavior and market trends.
8. Public Speaking and Presentation Skills: The ability to effectively present ideas and strategies to stakeholders and larger audiences can be advantageous for gaining buy-in and support.