Product Manager

  • 35870
  • Non-Life - Data Science
  • |
  • New York, United States
  • |
  • Sep 18, 2019
  • Coordination of a large scale but iterative data inventory program across a variety of departments, regions, databases and data type.
  • Selection, configuration and roll-out of a global data cataloging and governance solution.
  • Partnering with data engineering leads to support the build out an ETL framework into a unified data lake for historical and future datasets.
  • Contribute to a variety of additional data strategy initiatives such as data acquisition processes, data sharing technologies, monitoring applications, entity resolution solutions and the construction of internal knowledge graphs.
  • Participate in or lead the full product lifecycle for designated product areas, including research, scoping, prototyping, validation, productizing, and rollout. This includes management of blockers, dependencies, and risk factors across engineers, data scientists and analysts.
  • Understand the key value drivers for your product areas and demonstrate progress against success metrics to ensure our products are delivering value as expected.
  • Collaborate with product stakeholders across the development lifecycle communicating the overall product vision, incremental progress and changes in performance metrics.
  • Build internal documentation, supporting processes, and presentations around product specifications and capabilities.
  • Conduct market research and monitor competition to generate insights that can guide product roadmap and future opportunities within data strategy.
  • 5+ years of professional work experience.
  • 2+ years of experience in a product management role, preferably at a data company.
  • Bachelor’s degree required preferably in a quantitative discipline such as computer science, engineering, mathematics or physics.
  • Experienced and enthusiastic working with data in a variety of manners spanning proficiencies in Excel, SQL, API requests and basic scripting in Python or R.
  • Familiar with agile development principles.
  • Excellent communication skills. Be comfortable talking with technologists, stakeholders, clients and executives, both in-person and remote. Write clearly and succinctly. Listen well.
  • Conversant about technology and understand the effort required to deliver enterprise grade platforms, with a focus on data quality.
  • Comfortable working at a high level, down to the lowest levels including writing JIRA tickets and providing support if necessary.
  • Leadership skills, client-facing experience and the ability to influence internal and external stakeholders.
  • Experience with documentation management and maintenance.
  • Preferred:
  • Experience with cloud technologies such as AWS/ Google Cloud / Microsoft Azure.
  • Familiarity with Spark concepts such as Spark SQL, data frames, RDDs and cluster management.
  • Knowledge of data science platforms such as Databricks and DataRobot.