Hire Amy L. - AI Product Manager

Product Planning & Strategy Development: Responsible for defining the long-term vision and short-term goals for AI products, and developing product roadmaps aligned with corporate strategy. Requirements Gathering and Analysis: Talk to customers, users and internal teams to gather feedback and pain points to translate into specific product requirements documents. Cross-functional Collaboration: Coordinate with multiple departments such as Data Science, Engineering, Design, and Marketing during development and advancement to ensure products are delivered on time. Project Management: Responsible for project lifecycle management, ensuring on-time and on-budget delivery, and managing project risks and changes. Market Positioning and Launch: Develop market strategies including pricing, marketing and go-to-market plans, and be responsible for evaluating product performance after launch. Data Driven Decision Making: Leverage data analytics to track product performance and user engagement to help make decisions and optimize product features. Stay in sync with industry trends: Keep abreast of AI technology developments and market trends to support product innovation.

Model development and optimization: design, develop and implement complex machine learning models and algorithms to solve real business problems. Optimize model performance, including improving accuracy, reducing computational overhead, and accelerating the training process. Data Processing & Feature Engineering: responsible for data collection, cleansing, preprocessing and feature engineering to ensure quality and consistency of model input data. Develop automated data processing pipelines to ensure smooth data delivery to machine learning models. Model Deployment and Maintenance: Deploy machine learning models to the production environment to ensure stability and reliability of the models in real-world applications. Monitor model performance and perform regular updates and maintenance to ensure models remain accurate and valid over time. Research and Innovation: Conduct research on cutting-edge technologies and track the latest machine learning and artificial intelligence trends. Apply new technologies and methods to continuously improve existing systems and models. Technical Leadership & Mentoring : Mentor and train team members to improve the technical level and productivity of the entire team. Participate in the hiring process to assess candidates' technical skills and fit. Cross-team Collaboration: work closely with product managers, data scientists, data engineers, and other stakeholders to understand business needs and deliver technical solutions. Ensure machine learning programs are aligned with overall company strategy and goals.

Data Infrastructure Design and Development: design, build, and maintain data pipelines to ensure that data can be efficiently and securely transferred from a variety of sources to a data warehouse or data lake. Develop and optimize data storage solutions to support analytics and machine learning needs. Data Integration and Management: integrate various internal and external data sources to ensure data continuity and consistency. Manage ETL (Extract, Transform, Load) processes to ensure data quality and integrity. Data Processing & Transformation : Write scripts and programs to clean, transform and preprocess data to make it suitable for analysis and modeling. Process and analyze large-scale datasets using Big Data technologies (e.g. Hadoop, Spark, etc.). Database administration: design and maintain relational databases (e.g. MySQL, PostgreSQL) and non-relational databases (e.g. MongoDB, Cassandra). Optimize database performance to ensure efficient data querying and storage. Data Security and Privacy: Implement data security measures to protect sensitive data from unauthorized access and leakage. Ensure that data processing processes comply with relevant laws and regulations and industry standards (e.g., GDPR, HIPAA).