Data Scientist I Resume Sample

Rachel Martinez

rachel@martinez.sci

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(312) 555-0425

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linkedin.com/in/rachel-martinez-datascience

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github.com/rachelmartinez

Data Scientist I

Data Scientist I with 2 years developing predictive models and conducting data analysis. Proficient in Python, SQL, ML algorithms, and statistical analysis. Built 6 ML models with 85%+ accuracy, delivered 10+ business insights driving $500K revenue, and automated 8 analysis workflows. Passionate about machine learning, A/B testing, and data-driven decision making.

WORK EXPERIENCE

Data Scientist I

Nov 2023 – Present

E-Commerce Startup

ML Model Development: Built 6 production ML models including customer churn predictor (87% accuracy), product recommendation engine (15% CTR lift), and demand forecasting model (82% MAPE)
Business Impact & Insights: Delivered 10+ data-driven insights driving $500K incremental revenue, identified $200K cost savings opportunity through pricing analysis
Experimentation & Analysis: Designed and analyzed 12 A/B tests improving conversion by 18%, conducted cohort analysis and user segmentation for marketing strategy

Data Science Intern

Jun 2022 – Oct 2023

Marketing Analytics Firm

Predictive Modeling: Developed customer lifetime value (LTV) model using regression, built lead scoring model with random forest achieving 80% precision
Statistical Analysis: Conducted exploratory data analysis, hypothesis testing, and correlation analysis for 5 client projects, visualized insights using Tableau
Automation & Tools: Automated 8 recurring analyses with Python scripts, built data pipelines extracting data from APIs and databases for ML model training

SKILLS & COMPETENCIES

CERTIFICATIONS

TensorFlow Developer Certificate

Aug 2024

TensorFlow

EDUCATION

Bachelor of Science in Data Science

2019-2023

Northwestern University

Evanston, Illinois

Machine Learning
Statistics

Tools to build your Data Scientist I resume

Copy and adapt these proven examples to create a resume that stands out.

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Resume Headlines

Use these attention-grabbing headlines to make a strong first impression.

Data Scientist I | ML & Python | 6 Models Built, 87% Accuracy, $500K Revenue Impact

Data Scientist | Predictive Modeling & A/B Testing | 18% Conversion Improvement

Data Scientist I | Machine Learning & Statistical Analysis | E-Commerce Expert

Data Scientist | Python, scikit-learn, SQL | Driving Data-Driven Decisions

Data Scientist I | ML Models & Business Insights | 85%+ Model Accuracy

Data Scientist | Experimentation & Analytics | 12 A/B Tests, 18% Lift

💡 Tip: Choose a headline that reflects your unique value proposition and matches the job requirements.

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Power Bullet Points

Adapt these achievement-focused bullets to showcase your impact.

ML Model Development & Deployment

• Built 6 production ML models including customer churn predictor (87% accuracy), product recommendation engine (15% CTR lift), and demand forecasting model (82% MAPE)

• Developed lead scoring model using random forest achieving 80% precision and 75% recall, increasing qualified leads by 25% and sales efficiency

• Built customer lifetime value (LTV) prediction model using regression identifying high-value customers enabling targeted $200K marketing campaign

• Implemented gradient boosting classifier (XGBoost) for fraud detection achieving 92% recall catching 30% more fraudulent transactions

Business Impact & Insights

• Delivered 10+ data-driven insights driving $500K incremental revenue through pricing optimization, customer segmentation, and product recommendations

• Identified $200K cost savings opportunity through pricing analysis finding optimal discount strategies and reducing unnecessary promotions

• Analyzed customer segments discovering 20% of users drive 60% of revenue enabling targeted retention campaigns improving LTV by 18%

• Built executive dashboards visualizing key metrics (churn, revenue, conversion) enabling data-driven decisions across 5 business units

Experimentation & Statistical Analysis

• Designed and analyzed 12 A/B tests improving conversion by 18%, implementing proper statistical tests (t-tests, chi-square) and power analysis

• Conducted cohort analysis and user segmentation for marketing strategy identifying 3 high-value segments driving 70% of revenue

• Performed exploratory data analysis (EDA), correlation analysis, and hypothesis testing for 5 client projects uncovering actionable insights

• Implemented multi-armed bandit algorithm for dynamic pricing optimization improving revenue by 12% vs static A/B testing

Feature Engineering & Data Preparation

• Engineered 30+ features for ML models including time-based aggregations, interaction features, and text embeddings improving model performance by 15%

• Built data pipelines extracting data from APIs and databases, cleaning and transforming 500K+ records daily for ML model training

• Automated 8 recurring analyses with Python scripts reducing analysis time from 4 hours to 15 minutes and improving data freshness

• Handled missing data, outliers, and class imbalance using SMOTE, undersampling, and imputation techniques improving model robustness

💡 Tip: Replace generic terms with specific metrics, technologies, and outcomes from your experience.

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Resume Writing Tips for Data Scientist Is

Emphasize ML Model Development and Accuracy

Entry-level data scientists are judged on execution. Highlight: models built (6), accuracy metrics (87%), model types (classification, regression, recommendation). Show you ship production ML models, not just notebooks—you deploy to real users.

Quantify Business Impact, Not Just Technical Metrics

Connect ML to revenue. Include: revenue impact ($500K), conversion lifts (18%), cost savings ($200K), users affected. Avoid "built a model"—show how your model drove business value. Data science serves business outcomes.

Show Statistical Rigor and Experimentation

Data science requires stats foundation. Include: A/B testing (12 tests), hypothesis testing, experimental design, statistical significance. Show you understand causality and inference, not just correlation—demonstrates maturity beyond ML hype.

Balance Supervised Learning with Exploratory Analysis

List 10-12 skills covering ML (scikit-learn, TensorFlow), stats (A/B testing, hypothesis testing), data (pandas, SQL), and visualization (Tableau). Show T-shaped: depth in ML with breadth in stats, analysis, and communication.

Highlight Modern ML Stack and Tools

Show current tech: Python (pandas, scikit-learn), SQL, TensorFlow/Keras, Git, Jupyter. Include feature engineering, model evaluation, and deployment basics. Entry-level means fundamentals—show you know the core ML workflow end-to-end.

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Essential Skills & Keywords

Include these skills to optimize your resume for ATS systems and recruiter searches.

Machine Learning

Supervised Learning Classification & Regression scikit-learn Random Forest XGBoost Model Evaluation

Programming & Data

Python pandas NumPy SQL Data Wrangling ETL Basics

Statistical Analysis

A/B Testing Hypothesis Testing Statistical Inference Experimental Design Probability & Statistics Power Analysis

Deep Learning (Basics)

TensorFlow Keras Neural Networks (Basics) Deep Learning Fundamentals

Feature Engineering

Feature Engineering Feature Selection Data Preprocessing Handling Missing Data Class Imbalance (SMOTE) Data Transformation

Visualization & Communication

Data Visualization Tableau Matplotlib & Seaborn Storytelling with Data Jupyter Notebooks Exploratory Data Analysis

ML Operations

Model Deployment (Basics) Git & Version Control Model Monitoring Performance Metrics Cross-Validation

Best Practices

Agile/Scrum Code Reviews Documentation Collaboration Business Acumen

💡 Tip: Naturally integrate 8-12 of these keywords throughout your resume, especially in your summary and experience sections.

Why this resume works

Role-Specific Strengths

ML model development fundamentals: Built 6 models with 85%+ accuracy—demonstrates hands-on ML experience beyond just analysis
Business impact focus: $500K revenue impact—shows connection between data science work and business outcomes
Statistical rigor: A/B testing, hypothesis testing, confidence intervals—demonstrates statistical foundation
End-to-end data science workflow: Data exploration, feature engineering, modeling, deployment—shows complete DS lifecycle understanding

✓ ATS-Friendly Elements

Entry-level keywords: "machine learning," "Python," "SQL," "predictive modeling," "A/B testing," "statistical analysis"
Action verbs: Built, Developed, Analyzed, Implemented, Automated
Technologies: Python, scikit-learn, pandas, SQL, TensorFlow, Git
Practices: feature engineering, model evaluation, experimentation, data visualization
Quantified contributions: models built, accuracy achieved, revenue impact

✓ Human-Readable Design

Summary emphasizes ML execution: built models, delivered insights, automated workflows
Metrics scaled appropriately: 6 models, 85% accuracy, $500K impact, 10 insights
Experience shows progression from intern to DS I
Skills balance ML (scikit-learn), stats (hypothesis testing), and tools (SQL, pandas)
Recent degree with data/ML focus signals entry level

💡 Key Takeaways

Entry-level data scientists should emphasize ML model development and business impact
Quantify your work: models built, accuracy metrics, revenue impact, insights delivered
Show statistical foundation: A/B testing, hypothesis testing, experimental design
Highlight modern ML stack: Python, scikit-learn, TensorFlow, pandas, SQL
Balance supervised learning (classification, regression) with exploratory analysis

📈 Career Progression in Data Science

See how Data Science roles evolve from model development to ML strategy.

📊 Data Scientist I (Current) 🤖 Data Scientist II → 🚀 Senior Data Scientist →

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