Manager Talent Insights Advanced Analytics

• A minimum of a Bachelor's degree is required. Master’s degree or PhD is preferred.
• A minimum of 8 years of experience in leading data science and analytics projects. Utilization of the full stack of data science tools is preferred (SQL, Python, Tableau).
• Demonstrated knowledge in data science tools is required; tools such as Cloud IDEs, Cloud storage, Cloud computing platforms, Visualization software and automation software.
• Experience in judicious use of compute, cloud storage and bandwidth in projects is required.
• Experience in the full project lifecycle, inception, conceptualization, iteration cycles, deployment and after-action reviews is required.
• Demonstrated experience in stakeholder management in a matrix organization is required. Strong communication skills are a must for executive connects and engagements.
• People leadership experience is required. Experience in leading 3 or more technical team members is preferred.
• Required to have the ability to bring to life data and analytics to drive actionable business insights.
• Having a sizeable portfolio of projects that demonstrate leading data science initiatives for customers and clients is preferred.
• Experience in a global services setting or consulting for internal teams and/or clients is preferred.

• Hire, retain and engage experienced analysts to advance the data science agenda of TA.
• Train team members in the most suitable technologies and methodologies for data science.

• Manage the overall portfolio of projects for all products that TI Advanced Analytics will deliver. Systematically manage, oversee, and prioritize multiple projects and activities, while triaging ad-hoc requests. Conduct After Action Reviews (AARs) for all projects.
• Innovate and deliver analytics and insights needed for Quarterly Business Reviews.
• Own the democratization of external talent insights for TA, BUHR, and Hiring Managers via dashboards and other data science products.

• Facilitate stakeholder engagement to ensure the business value of TI data science products and initiatives.
• Connect with other leaders within and outside of TA to stay updated on current trends, needs within the organization, and developments in the data science and business space.

• Ensure full data science stack capability (Acquisition, Engineering, Storage, Analysis, Visualization).
• Ensure the readiness of the team to integrate and maximize AI within workflows and products.
• Stay current with leading external analytical capabilities and bring new knowledge into the organization. Maintain a good understanding of the latest data science technologies to iteratively improve the tech stack, architecture, and implement new libraries for more effective capabilities.
• Acquire and assimilate technologies and platforms to enhance Data Science and AI capabilities.
• Oversee external talent data sources and platforms.
• Drive continuous improvement through agentic AI and intelligent automation to fully automate tasks performed by machines.
• Champion data integrations across diverse data streams in HR.
• Ensure compliance of the projects through data and tech governance.

• Hire, retain and engage experienced analysts to advance the data science agenda of TA.
• Train team members in the most suitable technologies and methodologies for data science.

• Manage the overall portfolio of projects for all products that TI Advanced Analytics will deliver. Systematically manage, oversee, and prioritize multiple projects and activities, while triaging ad-hoc requests. Conduct After Action Reviews (AARs) for all projects.
• Innovate and deliver analytics and insights needed for Quarterly Business Reviews.
• Own the democratization of external talent insights for TA, BUHR, and Hiring Managers via dashboards and other data science products.

• Facilitate stakeholder engagement to ensure the business value of TI data science products and initiatives.
• Connect with other leaders within and outside of TA to stay updated on current trends, needs within the organization, and developments in the data science and business space.

• Ensure full data science stack capability (Acquisition, Engineering, Storage, Analysis, Visualization).
• Ensure the readiness of the team to integrate and maximize AI within workflows and products.
• Stay current with leading external analytical capabilities and bring new knowledge into the organization. Maintain a good understanding of the latest data science technologies to iteratively improve the tech stack, architecture, and implement new libraries for more effective capabilities.
• Acquire and assimilate technologies and platforms to enhance Data Science and AI capabilities.
• Oversee external talent data sources and platforms.
• Drive continuous improvement through agentic AI and intelligent automation to fully automate tasks performed by machines.
• Champion data integrations across diverse data streams in HR.
• Ensure compliance of the projects through data and tech governance.

Johnson & Johnson