GOALS

• Implement a robust and scalable recruiting process that can support the company's aggressive hiring plans in the fast-paced FinTech industry
• Leverage data and analytics to track key hiring metrics, identify bottlenecks, and continuously improve the efficiency and effectiveness of recruiting efforts
• Foster a positive candidate experience that reflects the company's employer brand and values, even for candidates who are not ultimately hired

Frustrations

• Dealing with a high volume of applicant submissions, making it difficult to manage and track candidates effectively
• Lacking the budget and resources for a large HR team or expensive recruiting tools, leading to manual and time-consuming processes
• Balancing the need for speed and efficiency in hiring with the importance of maintaining a high-quality candidate experience

GOALS

• Identify and attract highly skilled engineers with specialized expertise in artificial intelligence and machine learning, a highly competitive talent pool
• Collaborate effectively with recruiters and hiring managers to ensure a smooth and efficient interview process
• Advocate for and implement inclusive hiring practices that promote diversity within the engineering team, recognizing the value of diverse perspectives and experiences

Frustrations

• Competing with large tech companies and well-funded startups for a limited pool of qualified AI/ML engineers
• Spending significant time on resume screening and initial phone interviews, taking away from core engineering work and project deadlines
• Encountering bias in the hiring process, whether conscious or unconscious, which can lead to a lack of diversity and potentially miss out on talented candidates