Episode 2: In Conversation with Shervan Vafa

In this second episode of CareerQuest, our featured guest,  Shervan Vafa, Director of AI & Analytics at Wiser Solutions shares his journey from an economics background to leading data science and AI teams at major companies like Amazon and TD Bank. He provides actionable insights for those looking to break into data science, excel in leadership, and navigate the evolving landscape of AI-driven industries.

Key Takeaways:

Shervan’s Career Journey:

• Transitioned from economics to data science, leveraging quantitative modeling and problem-solving skills.

• Gained experience in financial services, ad tech, and e-commerce, eventually leading high-impact AI teams.

• Stresses the importance of combining technical skills with strategic thinking.

Breaking into Data Science:

• Develop a structured learning path with hands-on projects.

• Leverage AI tools like ChatGPT for interview prep and problem-solving.

• Gain experience through freelance work, internships, or contributing to open-source projects.

• Align unique domain expertise (e.g., finance, healthcare, psychology) with data roles.

Technical & Soft Skills for Success:

• Core skills: Python, SQL, ML fundamentals, data structures, experimentation techniques.

• Understanding data pipelines and MLOps is increasingly valuable.

• Communication and structured problem-solving are essential for career progression.

• AI literacy is a must—knowing how to evaluate AI-generated outputs is critical.

Leadership & Career Growth:

• Transitioning from IC (individual contributor) to leadership requires balancing execution with strategic vision.

• Navigating ambiguity is key—asking the right questions and prioritizing effectively.

• Hiring trends favor well-rounded candidates who understand business impact, not just technical execution.

• Emerging roles in AI ethics, governance, and compliance are creating new career opportunities.

The Future of Data Science Careers:

• AI will be embedded across industries—data skills will remain in demand, even as job titles evolve.

• Experimentation, model interpretability, and AI ethics are growing areas of focus.

• Professionals who integrate AI into their workflows will have a competitive edge.