Quantitative Analyst | Python, Trading Algorithms & Data Analysis

Jalen Carter

Work Experience

Automation  Database Intern at Startup Co Enterprise

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 Built Python app to extract and route customer email data into a Knack NoSQL database, boosting throughput by 30%  Created custom ETL pipeline that reduced manual input by 20%  Streamlined workflows for data handling across departments  Documented workflows and collaborated with technical and non-technical teams

IT Support Assistant at University IT Department

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 Managed 80-node network and deployed OS updates, maintaining 95% uptime  Upgraded core switches (25% port increase) and handled tier-1 IT issues for staff  Assisted with university web development and server monitoring

Senior Math Project  Algorithmic Trading System at University Project

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 Developed a quantitative trading strategy using technical indicators like RSI, moving averages, and Fibonacci retracements  Backtested strategy and outperformed the SP 500 over 5 years (96% return vs. 91%)  Engineered for lower volatility (Beta = 0.682) and improved drawdown protection (Max DD: 26.5%)  Achieved a Sharpe ratio of 0.61 with an 80% win rate  Used Python (Pandas) for signal generation, simulation, and performance tracking

Summer Research App Developer  Machine Learning at University Research Lab

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 Built iOS app using Swift to predict watermelon sweetness via signal and image processing  Trained linear regression model in MATLAB, achieving 92% prediction accuracy  Presented at Central Arkansas Undergraduate Research Symposium

Education

Bachelor of Science in Computer Science  Applied Mathematics (Double Major)

University of Central Arkansas

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GPA: 3.7

Skills

Technical Skills

  • Python
  • MATLAB
  • Swift
  • Java
  • TensorFlow
  • SQL

Soft Skills

  • Collaboration
  • Mentoring
  • Documentation
Score
75/100
Work Experience
75/100
Education
70/100
Projects
0/100
Certifications
0/100
Header
80/100
Profile
70/100
Skills
70/100
Add a professional summary or objective section to provide a concise career overview.
Include consistent and complete employment dates and locations in work experience for better ATS parsing.
Add contact phone number in the header to improve recruiter outreach and ATS compatibility.
Include a dedicated projects section to highlight key technical initiatives and achievements.
Add certifications or relevant licenses to strengthen professional credibility.
Strong quantitative and programming skills demonstrated by algorithmic trading system development and machine learning model training.
Use of measurable metrics (e.g., 30% throughput improvement, 92% regression accuracy) to highlight impact.
Diverse technical toolset including Python, MATLAB, Swift, TensorFlow, and SQL.
Experience collaborating across technical and non-technical teams, showing communication skills.

Work Experience

75%
Work descriptions include specific accomplishments with measurable impacts (e.g., 'boosting throughput by 30%'), which is very effective.
Job titles and company names are clear, but adding employment types (e.g., Internship, Part-time) and locations would enhance context.
Use consistent date formatting and ensure all start dates are included to improve ATS parsing and timeline clarity.

Education

70%
Education section includes degree, field of study, institution, and GPA, which is good for ATS and recruiters.
Add start date and location of the institution for completeness and improved ATS parsing.
Clarify the double major formatting for clarity (e.g., 'Computer Science and Applied Mathematics').

Projects

0%
Section missing

Certifications

0%
Section missing

Header

80%
The header includes a clear professional headline with relevant keywords like 'Quantitative Analyst', 'Python', and 'Trading Algorithms' which enhances ATS detection.
Add a phone number or alternative contact method to improve recruiter accessibility and ATS parsing.
Consider adding a professional summary or objective to provide a quick snapshot of qualifications.

Profile

70%
Profile highlights contain strong technical achievements with quantifiable metrics, which is excellent for both ATS and recruiters.
Expand the summary section with a concise paragraph summarizing core competencies and career goals to improve readability.
Use consistent formatting for bullet points and avoid special characters that may disrupt ATS parsing (e.g., replace '␡' with standard characters).

Skills

70%
Hard skills are well-listed with relevant programming languages and tools, aiding keyword matching in ATS.
Expand on soft skills by providing examples or context within work experience to demonstrate these competencies.
Consider grouping skills into categories (e.g., Programming Languages, Tools, Methodologies) for better readability.