Splash · 3 hours ago

Senior Product Manager

United States

Full-time

Remote

Senior Level

5+ years exp

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Responsibilities

Strategic Leadership: Ability to wear multiple hats as a strategic visionary leader in the field of data engineering, advanced analytics, and machine learning.

Market Research: Conduct thorough research on fintech data science industry trends to identify opportunities for leveraging advanced analytics, artificial intelligence, and machine learning to address specific needs.

Roadmap Development: Develop a compelling roadmap for advanced analytics and machine learning, outlining the future and long-term strategy for data science within the organization.

Prioritization and Coordination: Prioritize work for data analytics, engineering, and data science teams, and articulate any roadmap dependencies across other engineering branches.

Product Backlog Management: Manage and articulate a product backlog for data science and data engineering teams, ensuring alignment with overall business goals.

Technical Proficiency: Possess deep understanding and technical proficiency in data engineering, advanced analytics, and machine learning to set up Minimum Viable Products (MVPs) and differentiate between value-adding initiatives and those with minimal impact.

Domain Knowledge: Demonstrate business domain knowledge in the fintech industry or similar domains.

Autonomous Impact: Thrive on the opportunity to work autonomously, driving impactful initiatives in a fast-growing, ever-evolving business environment.

Technical Skills: Extensive knowledge in SQL, business intelligence tools (e.g., Tableau, Power BI, Sisense), Python and relevant packages for machine learning, and familiarity with cloud AWS infrastructure, data engineering, and warehousing tools (e.g., Snowflake, Redshift, AWS Firehose, GitHub, and data build tools like dbt).

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Data EngineeringAdvanced AnalyticsMachine LearningSQLBusiness Intelligence ToolsPythonAWS InfrastructureFintech Industry KnowledgeProduct Management

Required

Proven experience (5+ years) in a Senior Product Management role with a focus on data engineering, advanced analytics, and machine learning.

Strong track record of successfully developing and executing strategic roadmaps for data science initiatives.

Exceptional communication and leadership skills, with the ability to effectively prioritize and coordinate cross-functional teams.

Ability to thrive in a fast-paced, dynamic environment and drive autonomous impact.

Ability to wear multiple hats as a strategic visionary leader in the field of data engineering, advanced analytics, and machine learning.

Conduct thorough research on fintech data science industry trends to identify opportunities for leveraging advanced analytics, artificial intelligence, and machine learning to address specific needs.

Develop a compelling roadmap for advanced analytics and machine learning, outlining the future and long-term strategy for data science within the organization.

Prioritize work for data analytics, engineering, and data science teams, and articulate any roadmap dependencies across other engineering branches.

Manage and articulate a product backlog for data science and data engineering teams, ensuring alignment with overall business goals.

Possess deep understanding and technical proficiency in data engineering, advanced analytics, and machine learning to set up Minimum Viable Products (MVPs) and differentiate between value-adding initiatives and those with minimal impact.

Demonstrate business domain knowledge in the fintech industry or similar domains.

Thrive on the opportunity to work autonomously, driving impactful initiatives in a fast-growing, ever-evolving business environment.

Extensive knowledge in SQL, business intelligence tools (e.g., Tableau, Power BI, Sisense), Python and relevant packages for machine learning, and familiarity with cloud AWS infrastructure, data engineering, and warehousing tools (e.g., Snowflake, Redshift, AWS Firehose, GitHub, and data build tools like dbt).