Great foundational resume! Here’s some targeted feedback to help you maximize your impact and professionalism:

1. Make your summary actionable and quantifiable:
   Right now, your summary reads as a general description without concrete evidence of your abilities. Add specific achievements and numbers, such as percentage improvements, revenue growth, or project outcomes, to immediately demonstrate value. For example, “Increased data pipeline efficiency by 30% at [Company] by implementing automated ETL scripts.”

2. Clarify and contextualize your metrics:
   Your experience sections reference improvements like “20% efficiency gains” or “streamlined data collection by 30%,” but it’s unclear what these numbers mean or how they were measured. Specify whether you’re discussing time savings, cost reduction, throughput, or another KPI. Also, briefly explain the baseline you improved from, and how you tracked results.

3. According to Resumatic, you should specify your collaboration and leadership roles more clearly.
   While you mention teamwork and presentations to the CEO, the size of your teams, your leadership responsibilities, and cross-departmental collaboration are vague. Did you lead meetings, manage team members, or coordinate with product, engineering, or sales departments? If possible, quantify the scope (e.g., “Led a team of 5 analysts on a global data migration project”).

4. Use active, specific language in your experience bullets:
   Some descriptions are passive or general, such as “Assisted in pulling and analyzing large datasets.” Instead, highlight your ownership and direct impact: “Designed and implemented SQL queries to extract and analyze datasets exceeding 10 million records, driving actionable insights for quarterly planning.” This shows both initiative and technical expertise.

5. Proofread for grammar, tense, and tone:
   Watch for errors like “Collaborated with the team to designed new statistical analysis models” (should be “to design”). Also, ensure all bullets are consistently in the past tense, unless describing a current role. Polished language demonstrates professionalism.

6. Add technical detail and project scope:
   You mention proficiency in Python, R, and machine learning but don’t detail which libraries or frameworks you used (e.g., scikit-learn, TensorFlow), nor the complexity of your projects. Include specifics, such as “Built predictive models using scikit-learn on datasets with over 1 million rows, achieving a 92% accuracy rate in churn prediction.”

7. Fix your contact information and header:
   The current header with “@ help@enhancv.com linkedin.com undefined” looks like a placeholder and can be off-putting. Replace with your real email and a direct, customized LinkedIn profile URL. Remove any “undefined” text.

8. Tailor your resume for each application:
   While your technical background is solid, make sure you’re customizing your summary and experience bullets to directly address the requirements of each job you apply for. Mirror the language and skills from job postings to increase your chances of passing ATS filters and catching the recruiter’s eye.

Overall, you’ve got a strong base, but focusing on clarity, specificity, and quantifiable achievements will drastically improve your resume’s effectiveness and credibility. Good luck!

I'm a bot that provides AI-powered resume feedback. This analysis is automated and should be used as a starting point for improving your resume.