RDI Lead Data Product Manager

As an RDI Lead Data Product Manager, you’ll play a crucial role in guiding the development of our RDI products used by our clients and advisory team. RDI (Rewards Data Intelligence) offerings are compensation-related data products, such as compensation surveys, HR analytics and insights. These products play a critical role in large, multinational organizations’ strategies to attract & retain talent as well as respond to the increasing demands of pay equity and pay transparency. Your responsibilities will span across various areas, from defining regional and industry specific needs to planning long range data product initiatives and building agile execution plans. Here are the key duties and qualifications for this role:

1. Product Vision and Strategy: Contribute to the long-term vision and strategy for your assigned products.
2. Identify Opportunities: Conduct market research, collaboration with internal stakeholders, collected competitive intelligence, and interview clients to identify product opportunities which may be new products or enhancements.
3. Value Estimation: Estimate the value and revenue potential of product opportunities and formulate successful development, enablement, and launch strategies.
4. Requirements and Designs: Maintain product requirements for your assigned products, collaborating closely with technical and operations teams.
5. Product Roadmap: Manage the product roadmap and prioritize features and enhancements, utilizing agile style project management approaches for your assigned products.
6. KPIs: Take responsibility for key performance indicators, especially revenue, churn, and utilization for your assigned products.
7. Product Evangelization: Be an advocate for your products internally across the enterprise, externally in the industry, and with analysts.
8. Stakeholder Management: Establish and support regular cadences of bi-directional communication with various stakeholder groups to gather feedback and align on product decisions.
9. Inform and Validate Data Approaches: Understand and inform which data processing and presentation approaches should be utilized for which use cases within your product portfolio.  These may include, but are not limited to, predictive analytics, prescriptive analytics, natural language Processing (NLP), machine learning, generative AI, etc.
10. Client Empathy: Build client empathy via client interviews, steering committees, and feedback sessions to keep up with client needs and pain points in the compensation and rewards space. Evangelize these needs back to the business stakeholders.
11. Cross-Functional Collaboration: Collaborate with cross functional teams such as engineers, designers, advisory, sales, client success managers, & more to ensure alignment and execution of projects centered around the improvement of your products. 

Qualifications:

• Experience: 5+ years demonstrated experience in product management, particularly data related products.  Experience in the compensation or Human Resources space is highly desirable.
• Data Technology & Skills: Familiarity with data technologies such as data visualizations, Natural Language Processing (NLP), Machine Learning, generative AI. Comfort with big data approaches, such as data lakes, desirable. 
• Product Delivery: Experience with Epic definition, story mapping, backlog management, feature delivery, A/B Testing, etc.
• Communication Skills: Effective communication, speaking, and presentation skills are essential. Must be comfortable speaking with large and small groups, which could be internal stakeholders or clients. Must also be effective at explaining complex concepts simply and effectively.
• Data Skills: Knowledge of integration and data transformation best practices, API integrations/partnerships, and ETL processes very desirable. Real-time large-scale big data platform experience, such as working with data lake driven products, is also valuable.
• Analytics: Qualitative and quantitative data analysis skills are essential, along with a familiarity in working with both with structured and unstructured data. 
• Tools: MS Office Suite, particularly Excel, Powerpoint, & Power BI.