Structure your answer to directly map your education and experience to the role's requirements, using the specific course and deliverable as evidence. For each, clearly explain the technique, what you built, and the measurable impact. Then, proactively address any gaps with a concrete 90-day plan that includes milestones and learning objectives.
Pro tip: Quantify impact using metrics relevant to EY's data science work, such as model accuracy improvements, efficiency gains, or revenue impact, and tie them to business outcomes. Show self-awareness by acknowledging gaps and presenting a structured plan to close them, demonstrating adaptability and initiative.
Briefly summarize how your academic background and work experience collectively prepare you for the role, highlighting key skills and knowledge areas.
Select one relevant course and describe the technique learned, what you built (e.g., a model, analysis), and the measurable impact (e.g., improved prediction accuracy, cost savings).
Choose a concrete deliverable from the last two years, explain the technique used, what you built, and quantify its impact with metrics like time saved, revenue generated, or accuracy achieved.
Identify any gaps between your background and the role, then outline a 90-day plan with specific milestones (e.g., courses, certifications, projects) to close them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.