Maya Lin
Lead Data Scientist / Machine Learning EngineerAnalytical and results-driven Data Scientist with 6+ years of experience extracting insights from complex datasets and deploying machine learning pipelines. Expert in predictive modeling, deep learning architectures, Python/SQL, and cloud-based AI deployments (AWS/GCP). Proven ability to translate mathematical concepts into business growth.
Experience
- Architected and deployed a deep learning recommendation model that drove a 14% increase in user click-through rate (CTR) and generated $2.4M in additional revenue.
- Developed and optimized ETL pipelines in PySpark, processing over 10TB of clickstream data daily and reducing database latency by 20%.
- Led a team of 4 data analysts, standardizing automated model validation scripts and reducing model drift by 15%.
- Built predictive churn forecasting models with XGBoost, resulting in a proactive retention program that saved $950k in annual recurring revenue.
- Implemented NLP algorithms (BERT/Transformer models) to classify customer support tickets, automating 35% of manual routing.
Education
Stanford University
Skills
Deep Learning, Supervised Learning, NLP, Time Series Forecasting, A/B Testing, ETL Pipelines
Python, SQL, PySpark, PyTorch, TensorFlow, Scikit-Learn, AWS, Docker
Data Scientist Resume Example
Review this premium layout for a Data Scientist role. This design uses standard single-column rules and formatting parameters to guarantee clean parser scans.
Writing Guidelines for Data Scientists
- •Prioritize technical competencies: Order skill sets relative to matching keyword phrases in job descriptions.
- •Focus on metrics: Quantify your results (e.g. page speeds, savings, sales figures) in your bullet highlights.
- •Reverse Chronological: List experiences starting from the most recent to align with ATS filters.
Formatting Advice & FAQ
How do you format a machine learning or data science resume for ATS scanners?
To pass automated screening filters, describe your machine learning projects using plain text bullet points rather than embedding them in diagrams, sidebars, or info cards. Use standard terminology (e.g. 'supervised learning', 'deep learning model deployment') so semantic filters match your experience with key job requirements.
Where should online data science projects (like Kaggle or GitHub repositories) be listed on a resume?
Place your data science projects in a dedicated 'Projects' section below your work history. Write the title of the project and the direct URL link in standard text format. This enables recruiters and applicant tracking systems (like Greenhouse and Workday) to index and access your repository links cleanly.
Are tables or lists better for listing libraries (like PyTorch or Pandas)?
Lists are significantly better. Most ATS parsers (especially older versions of Taleo or Workday) read resumes from left to right. When libraries are listed in tables, the parser may concatenate text cells together, resulting in illegible terms like 'PythonSQLPandas'. Comma-separated list strings in single columns are parsed with 100% accuracy.