Worth AI is looking to solve the problem of revolutionizing decision-making with the power of artificial intelligence by developing data-driven features and infrastructure that power their analytics, machine learning, and platform capabilities.
Requirements
- Proven experience as a Product Manager + ML with 3+ years in data-centric or analytics-focused products
- Strong understanding of data platforms, ETL/ELT pipelines, and data warehousing concepts
- Familiarity with data tools such as Snowflake, BigQuery, dbt, Airflow, or similar technologies
- Experience working cross-functionally with data engineers, analysts, and scientists
- Track record of successfully shipping data products or platforms to internal or external users
- Comfort with SQL, data exploration, and metrics analysis to inform product decisions
- Experience aligning with senior leadership and translating business needs into data product strategies
Responsibilities
- Engage with internal and external users to uncover data-related needs, pain points, and opportunities
- Identify and prioritize data product opportunities, including data pipelines, APIs, dashboards, and reporting tools
- Partner with ML engineers to operationalize models and ensure seamless integration into customer-facing products
- Define metrics to measure model performance and business impact
- Translate user and business requirements into clear, actionable product use cases and specifications
- Partner with data engineering and data science teams to deliver models, infrastructure, and analytical tools
- Own the product backlog and ensure user stories are technically groomed and ready for development
Other
- Proven experience as a Product Manager + ML with 3+ years in data-centric or analytics-focused products
- Excellent written and verbal communication skills; able to distill complex topics for varied audiences
- Creative and strategic thinking with the ability to solve ambiguous problems
- Highly coachable, collaborative, and excited to challenge the status quo
- Ability to work closely with stakeholders across engineering, data science, design, and business units
