Job Description
Job Description:
Responsibilities:
Market Research: Assist in conducting market research and competitive analysis to identify trends, opportunities, and gaps in the AI/ML product landscape. Stay informed about industry developments, emerging technologies, and competitor offerings.
Product Ideation: Participate in brainstorming sessions and idea generation activities to contribute to the development of new AI/ML product concepts. Collaborate with cross-functional teams to explore potential use cases, features, and functionalities.
Requirements Gathering: Support senior product managers in gathering and documenting product requirements based on customer feedback, market research, and business goals. Translate user needs into actionable user stories and product specifications.
Product Development: Work closely with engineering, data science, and design teams to support the development and launch of AI/ML products. Assist in defining product roadmaps, prioritizing features, and tracking project timelines.
User Testing and Feedback: Assist in organizing user testing sessions and gathering feedback from early adopters and beta testers. Analyze user feedback and iterate on product features to improve usability, functionality, and user satisfaction.
Documentation and Communication: Help in creating product documentation, including user guides, release notes, and technical specifications. Communicate product updates, milestones, and key insights to internal stakeholders and external partners.
Project Coordination: Assist in coordinating cross-functional activities and ensuring alignment across teams. Participate in sprint planning, backlog grooming, and other agile ceremonies to support the product development process.
Qualifications:
Education: Bachelor's degree in computer science, engineering, business, or a related field. Additional coursework or certifications in AI/ML, product management, or related disciplines is a plus.
Technical Skills: Basic understanding of AI/ML technologies, algorithms, and applications. Familiarity with data analysis, statistical modeling, and machine learning concepts. Proficiency in tools such as Python, R, or TensorFlow is advantageous.
Analytical Skills: Strong analytical and problem-solving skills, with the ability to analyze data, draw insights, and make data-driven decisions. Comfortable working with quantitative data and conducting basic statistical analysis.
Communication Skills: Excellent written and verbal communication skills, with the ability to articulate technical concepts to both technical and non-technical audiences. Ability to communicate effectively with cross-functional teams and external stakeholders.
Collaboration: Team player with a collaborative mindset and willingness to learn and grow in a fast-paced environment. Ability to work effectively in a multidisciplinary team and contribute to team goals and objectives.
Adaptability: Flexible and adaptable to changing priorities and business needs. Ability to thrive in a dynamic and evolving industry landscape.
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