Data Scientist II Resume Example

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Data Scientist II Resume Sample

Nathan Chen
nathan@chen.sci
•
(650) 555-0440
•
linkedin.com/in/nathan-chen-datascience
•
github.com/nathanchen
Data Scientist II
Data Scientist II with 5 years developing ML systems and driving business impact. Led ML initiatives generating $5M+ revenue, deployed 15+ models to production, and improved model performance by 40%. Expert in deep learning, NLP, causal inference, and MLOps. Strong in technical leadership, experimentation strategy, and stakeholder collaboration.
WORK EXPERIENCE
Data Scientist II
Jun 2022 – Present
SaaS Unicorn
  • ML Systems & Revenue Impact: Led ML initiatives generating $5M+ annual revenue: built recommendation system increasing upsell by 35%, deployed churn prevention model saving $2M ARR
  • Production ML & MLOps: Deployed 15+ models to production using MLflow and SageMaker, established model monitoring and retraining pipelines, improved model latency from 800ms to 120ms (85% faster)
  • Experimentation & Causal Inference: Designed experimentation framework running 50+ A/B tests annually, applied causal inference (propensity scoring, diff-in-diff) for marketing attribution
Data Scientist I
Aug 2019 – May 2022
FinTech Scale-up
  • Deep Learning & NLP: Built NLP model for customer support ticket classification (92% F1 score) using BERT, developed fraud detection system using deep neural networks reducing false positives by 60%
  • Predictive Modeling: Developed customer LTV model with XGBoost achieving 15% improvement over baseline, built credit risk model using ensemble methods (AUC 0.89)
  • Feature Engineering & Optimization: Engineered 100+ features for ML models, improved model performance by 40% through hyperparameter tuning, feature selection, and ensemble techniques
SKILLS & COMPETENCIES
Python (Expert: pandas, NumPy, scikit-learn) | Deep Learning (TensorFlow, PyTorch) | NLP & Transformers (BERT, GPT) | SQL (Advanced) | Machine Learning (Advanced) | Causal Inference | MLOps (MLflow, Kubeflow) | AWS SageMaker | A/B Testing & Experimentation | Model Deployment | Feature Engineering | Spark MLlib
CERTIFICATIONS
AWS Certified Machine Learning Specialty
Dec 2023
Amazon Web Services
EDUCATION
Master of Science in Computer Science
2017-2019
Stanford University
Stanford, California
  • Machine Learning
  • Artificial Intelligence

Tools to build your Data Scientist II 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 II | ML Systems & MLOps | $5M+ Revenue Impact, 15 Models in Production
Mid-Level Data Scientist | Deep Learning & NLP | 35% Upsell Improvement
Data Scientist II | TensorFlow, PyTorch, AWS SageMaker | 85% Latency Reduction
Data Scientist | Advanced ML & Experimentation | 50+ A/B Tests Annually
Data Scientist II | Production ML Expert | $2M ARR Saved Through Churn Prevention
Data Scientist | End-to-End ML | Recommendation Systems, NLP, Causal Inference

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

⚡

Power Bullet Points

Adapt these achievement-focused bullets to showcase your impact.

ML Systems & Revenue Impact

• Led ML initiatives generating $5M+ annual revenue: built recommendation system increasing upsell by 35%, deployed churn prevention model saving $2M ARR
• Developed dynamic pricing model using reinforcement learning increasing revenue by $3M annually through optimal pricing across 10K+ products
• Built personalization engine using collaborative filtering and deep learning improving engagement by 45% for 500K+ users
• Created customer segmentation models using clustering and propensity modeling enabling targeted campaigns generating $1.5M incremental revenue

Production ML & MLOps

• Deployed 15+ ML models to production using MLflow and AWS SageMaker serving 1M+ predictions daily with 99.9% uptime
• Established MLOps infrastructure with model versioning, A/B testing, monitoring, and automated retraining reducing model drift by 70%
• Improved model latency from 800ms to 120ms (85% faster) through model optimization, caching, and serving infrastructure improvements
• Built CI/CD pipelines for ML with automated testing, validation, and deployment reducing model deployment time from 2 weeks to 2 days

Deep Learning & NLP

• Built NLP model for customer support ticket classification (92% F1 score) using BERT fine-tuning automating routing for 50K+ monthly tickets
• Developed sentiment analysis system using transformers analyzing 100K+ customer reviews enabling product improvements and reducing churn
• Implemented named entity recognition (NER) model extracting structured data from unstructured text with 88% precision
• Built document similarity model using BERT embeddings and vector search enabling semantic search across 1M+ documents

Experimentation & Causal Inference

• Designed experimentation framework running 50+ A/B tests annually with proper statistical power analysis and multiple testing corrections
• Applied causal inference techniques (propensity score matching, difference-in-differences) for marketing attribution and incrementality testing
• Implemented multi-armed bandit algorithms for dynamic feature experimentation improving conversion by 22% vs traditional A/B testing
• Conducted cohort analysis and survival analysis identifying key drivers of retention and informing product roadmap

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

📝

Resume Writing Tips for Data Scientist IIs

1

Emphasize End-to-End ML Ownership

Mid-level data scientists own systems, not just models. Highlight: deployed 15 models to production, established MLOps infrastructure, built experimentation frameworks. Show you own the full ML lifecycle—from research to production to monitoring.

2

Quantify Revenue and Business Impact at Scale

Connect ML to P&L. Include: revenue generated ($5M+), ARR saved ($2M), conversion lifts (35%), users impacted (500K+). Mid-level DS means business impact—show your models drive top-line and bottom-line outcomes.

3

Demonstrate Advanced ML and Depth

Show technical sophistication: deep learning (TensorFlow, PyTorch), NLP (BERT, transformers), recommendation systems, causal inference. Mid-level requires depth—go beyond scikit-learn to state-of-the-art techniques.

4

Show MLOps and Production Expertise

Production ML differentiates mid-level. Include: model deployment (SageMaker, MLflow), monitoring, versioning, CI/CD, latency optimization (85% faster). Show you build production systems, not just Jupyter notebooks.

5

List 12-15 Skills Across ML Stack

Cover Python (expert), deep learning (TensorFlow, PyTorch), NLP (BERT), ML platforms (SageMaker, MLflow), causal inference, A/B testing. Show T-shaped: deep ML expertise with broad competence in MLOps, experimentation, and business.

🎯

Essential Skills & Keywords

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

Machine Learning (Advanced)

Supervised & Unsupervised Learning Ensemble Methods XGBoost & LightGBM Recommendation Systems Time Series Forecasting Anomaly Detection

Deep Learning

TensorFlow PyTorch Neural Networks CNNs & RNNs Transfer Learning Model Optimization

NLP & Transformers

Natural Language Processing BERT & Transformers Text Classification Sentiment Analysis Named Entity Recognition Text Embeddings

ML Engineering & MLOps

MLflow AWS SageMaker Kubeflow Model Deployment Model Monitoring CI/CD for ML

Programming & Data (Expert)

Python (Expert) pandas & NumPy scikit-learn SQL (Advanced) PySpark Distributed Computing

Experimentation & Causal Inference

A/B Testing Experimental Design Causal Inference Propensity Score Matching Difference-in-Differences Multi-Armed Bandits

Feature Engineering & Optimization

Feature Engineering (Advanced) Hyperparameter Tuning AutoML Model Optimization Feature Selection Dimensionality Reduction

Best Practices

Technical Leadership Cross-Functional Collaboration Stakeholder Management Business Acumen Mentorship Documentation

💡 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 systems and production deployment: Deployed 15+ models to production—shows ownership beyond just notebook experiments
  • Business impact at scale: $5M revenue generation, 40% performance improvement—demonstrates significant business value
  • Advanced ML techniques: Deep learning, NLP, causal inference—shows technical depth beyond basic ML
  • MLOps and engineering mindset: Model monitoring, A/B testing frameworks, ML pipelines—demonstrates production ML maturity

✓ ATS-Friendly Elements

  • Mid-level keywords: "deep learning," "NLP," "production ML," "MLOps," "causal inference," "experimentation"
  • Action verbs: Led, Architected, Deployed, Optimized, Established
  • Business outcomes: revenue generation, model performance, prediction accuracy, business metrics
  • Technologies: Python, TensorFlow, PyTorch, MLflow, Kubernetes, AWS SageMaker
  • Demonstrates progression from DS I to DS II with increasing scope and impact

✓ Human-Readable Design

  • Summary balances technical depth with business outcomes
  • Metrics show broader scope: $5M revenue, 15 production models, 40% improvements
  • Experience demonstrates ownership: led initiatives, deployed systems, drove strategy
  • Shows both ML depth (deep learning, NLP) and breadth (MLOps, experimentation)
  • Technology choices show maturity: production frameworks, monitoring, versioning

💡 Key Takeaways

  • Mid-level data scientists own end-to-end ML systems, not just model development
  • Quantify scale and impact: revenue generated, models in production, performance improvements
  • Show advanced ML: deep learning, NLP, recommendation systems, causal inference
  • Demonstrate MLOps: model monitoring, versioning, CI/CD, A/B testing infrastructure
  • Balance technical execution with business strategy and stakeholder management

📈 Career Progression in Data Science

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

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