- Develop and implement strategies to improve the quality of talent design data quality.
- Develop and expand key AI products like Talent Design, skills engine, which are core to EF offerings.
- Monitor key data metrics with help of annotators and analyst team. Identify opportunities to improve data coverage and quality.
- Improving product features through customer feedback sessions and sales feedback. Work with partners / service providers to take feedback on data quality and align product direction accordingly
- Partner with engineering to develop and launch new features
- Identify and implement opportunities to improve the efficiency and effectiveness of the products.
- Represent the talent design, data quality, and copilot team at company-wide meetings, events and to customers.
- Develop and implement strategies to enhance the quality of talent design data.
- Develop and expand key AI products, such as titles and skills engines, which are central to EF offerings.
Performance Expectations:
- Data and analytical mindset:
- Data Literacy: Possessing a strong foundation in understanding and interpreting data. This includes the ability to collect, organize, analyze, and draw meaningful insights from data.
- Statistical Acumen: Familiarity with statistical concepts and methods for analysing data, including hypothesis testing, regression analysis, and experimental design.
- Critical Thinking: The ability to approach data with a skeptical and inquisitive mindset, questioning assumptions and identifying potential biases or limitations.
- Technical Proficiency: Familiarity with data analysis tools and software, such as spreadsheets, SQL, python.
- Customer and user focus: ensure products provide end customer value and seamless experience
- Product requirements gathering and analysis: Proactively identify opportunities of product improvement, especially from a data perspective
- Execution For Results: We are scaling fast and we have a process. While any process can always be improved, you will be driving releases and results through this process.
- Collaboration: your work and the product impacts and has dependencies on various, geographically separate, and cross-functional teams. Several teams have thought about copilot and this employee needs to collaborate on product and GTM as well as have a well-informed point of view
- Communication: This is a high-visibility role and clear succinct communication is expected. Executive updates, customer sessions are common
- Curiosity: show curiosity about LLM/GenAI applications and copilots, play with them or view demos, and have a point of view on how they are driving success e.g. Microsoft, GitHub, competitors, and other vertical apps

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