Data Scientist Resume Keywords: ML, AI & Analytics Skills List

Data science hiring has shifted. In 2024, companies want production ML experience, not just Kaggle competitions. Your resume keywords need to reflect this reality.

I've helped data scientists land roles at FAANG, top startups, and Fortune 500 companies. The ones who succeed understand that keyword strategy is about matching what companies actually need—not listing every library you've ever imported.

Here's the complete keyword guide for data scientist resumes in 2026. For the complete system on translating technical work into business impact bullets, see our Professional Impact Dictionary.

Programming Languages

Core Languages (Must-Have)

• Python — The dominant data science language
• SQL — Still essential for every data role
• R — Important for statistics-heavy roles

Supporting Languages

• Scala (Spark jobs)
• Julia (high-performance computing)
• Java (production systems)
• C++ (performance-critical ML)
• Bash/Shell scripting

How to List Languages

Weak: "Proficient in Python, R, SQL, Scala, Julia, Java"

Strong: "Built ML pipelines in Python, wrote production Spark jobs in Scala, designed analytics dashboards with SQL and R"

Machine Learning Frameworks

Deep Learning Frameworks

• TensorFlow
• PyTorch
• Keras
• JAX
• Hugging Face Transformers
• LangChain
• OpenAI API

Classical ML

• Scikit-learn
• XGBoost
• LightGBM
• CatBoost
• statsmodels

AutoML & MLOps

• MLflow
• Kubeflow
• SageMaker
• Vertex AI
• Weights & Biases
• DVC (Data Version Control)
• Feature stores (Feast, Tecton)

Specialized ML

• spaCy (NLP)
• NLTK
• OpenCV (Computer Vision)
• Detectron2
• YOLO
• Stable Diffusion
• LlamaIndex

Data Processing & Analytics

Data Manipulation

• pandas
• NumPy
• Polars
• Dask
• Vaex

Big Data Processing

• Apache Spark
• PySpark
• Databricks
• Apache Kafka
• Apache Airflow
• Prefect
• Dagster

Data Visualization

• Matplotlib
• Seaborn
• Plotly
• Bokeh
• Altair
• Tableau
• Power BI
• Looker
• Metabase

Statistical Tools

• SciPy
• statsmodels
• Stan (Bayesian)
• PyMC
• SPSS
• SAS
• Stata

Cloud Platforms & Infrastructure

AWS Data Science Stack

• SageMaker
• S3
• Redshift
• Athena
• Glue
• EMR
• Lambda
• EC2

Google Cloud Stack

• Vertex AI
• BigQuery
• Dataflow
• Dataproc
• Cloud Storage
• Pub/Sub

Azure Stack

• Azure ML
• Synapse Analytics
• Data Factory
• Azure Databricks
• Cognitive Services

Infrastructure Skills

• Docker
• Kubernetes
• Terraform
• CI/CD for ML
• Model serving (TensorFlow Serving, TorchServe)
• API development (FastAPI, Flask)

Statistical & ML Concepts

Supervised Learning

• Linear regression
• Logistic regression
• Decision trees
• Random forests
• Gradient boosting
• Support vector machines
• Neural networks
• Deep learning

Unsupervised Learning

• Clustering (K-means, DBSCAN, hierarchical)
• Dimensionality reduction (PCA, t-SNE, UMAP)
• Anomaly detection
• Association rules

Deep Learning Architectures

• CNNs (Convolutional Neural Networks)
• RNNs (Recurrent Neural Networks)
• LSTMs
• Transformers
• Attention mechanisms
• GANs
• Diffusion models
• Large Language Models (LLMs)

NLP Concepts

• Text classification
• Named entity recognition (NER)
• Sentiment analysis
• Topic modeling
• Word embeddings (Word2Vec, GloVe)
• BERT
• GPT
• Prompt engineering
• RAG (Retrieval Augmented Generation)
• Fine-tuning

Computer Vision

• Image classification
• Object detection
• Image segmentation
• OCR
• Video analysis
• Facial recognition

Time Series

• Forecasting
• ARIMA
• Prophet
• Seasonal decomposition
• Anomaly detection

Experimentation & Causal Inference

• A/B testing
• Hypothesis testing
• Statistical significance
• Confidence intervals
• Causal inference
• Propensity score matching
• Difference-in-differences
• Uplift modeling

Database & Data Storage Keywords

SQL Databases

• PostgreSQL
• MySQL
• SQL Server
• Snowflake
• BigQuery
• Redshift

NoSQL Databases

• MongoDB
• Cassandra
• DynamoDB
• Redis
• Elasticsearch

Data Concepts

• Data modeling
• ETL/ELT
• Data pipelines
• Data warehousing
• Data lakes
• Data governance
• Data quality

Action Verbs for Data Scientists

For Analysis Work

• Analyzed
• Investigated
• Explored
• Identified
• Discovered
• Quantified
• Measured
• Evaluated

For Model Building

• Developed
• Built
• Trained
• Designed
• Implemented
• Engineered
• Created
• Architected

For Deployment

• Deployed
• Productionized
• Scaled
• Automated
• Integrated
• Operationalized
• Monitored
• Maintained

For Impact

• Improved
• Increased
• Reduced
• Optimized
• Enhanced
• Accelerated
• Saved
• Generated

For Communication

• Presented
• Communicated
• Translated
• Visualized
• Documented
• Collaborated
• Influenced
• Advised

Keywords by Experience Level

Junior Data Scientist (0-2 years)

Focus on:

• Python, SQL basics
• Classical ML (scikit-learn)
• Data visualization
• Statistical fundamentals
• Jupyter notebooks
• Academic projects / Kaggle

Example keywords: Python, pandas, scikit-learn, SQL, data visualization, statistical analysis, regression, classification, Jupyter, exploratory data analysis

Mid-Level Data Scientist (3-5 years)

Add:

• Production ML experience
• Deep learning basics
• Cloud platforms
• A/B testing
• Stakeholder communication
• End-to-end project ownership

Example keywords: TensorFlow/PyTorch, AWS/GCP, A/B testing, model deployment, cross-functional collaboration, MLflow, feature engineering, business impact

Senior Data Scientist (6-10 years)

Add:

• ML system design
• Technical leadership
• Research to production
• Mentorship
• Strategic planning
• Complex experimentation

Example keywords: ML architecture, technical leadership, research productionization, experimentation platform, stakeholder management, team mentorship, ML strategy

Staff/Principal (10+ years)

Add:

• Org-wide ML strategy
• ML platform decisions
• Research direction
• Executive communication
• Build vs. buy decisions
• Industry thought leadership

Example keywords: ML strategy, platform architecture, research leadership, executive communication, technical vision, org-wide impact, ML best practices

Business & Soft Skills Keywords

Communication

• Data storytelling
• Executive presentations
• Stakeholder management
• Technical translation
• Documentation
• Cross-functional collaboration

Business Acumen

• Business metrics (KPIs, OKRs)
• ROI analysis
• Cost-benefit analysis
• Product sense
• Customer insights
• Market analysis

Problem-Solving

• Analytical thinking
• Hypothesis-driven
• Root cause analysis
• Critical thinking
• Creative problem-solving
• Strategic thinking

Leadership

• Technical mentorship
• Project leadership
• Team collaboration
• Knowledge sharing
• Best practices
• Process improvement

Domain-Specific Keywords

FinTech/Finance

• Risk modeling
• Credit scoring
• Fraud detection
• Algorithmic trading
• Portfolio optimization
• Quantitative analysis
• Time series forecasting
• Regulatory compliance

Healthcare/Biotech

• Clinical trials
• Drug discovery
• Medical imaging
• Electronic health records
• HIPAA compliance
• Survival analysis
• Genomics
• Bioinformatics

E-Commerce/Retail

• Recommendation systems
• Personalization
• Demand forecasting
• Price optimization
• Customer segmentation
• Churn prediction
• Lifetime value (LTV)
• Attribution modeling

AdTech/Marketing

• Click-through rate (CTR)
• Conversion optimization
• Attribution modeling
• Customer acquisition cost (CAC)
• Marketing mix modeling
• Audience targeting
• Real-time bidding

Social/Consumer

• User engagement
• Content recommendation
• Trust & safety
• Abuse detection
• Network analysis
• Viral prediction
• Sentiment analysis

LLM & Generative AI Keywords (2026 Priority)

These keywords are increasingly important:

LLM Skills

• Large Language Models
• GPT / ChatGPT
• Claude
• Llama
• Prompt engineering
• Few-shot learning
• Chain-of-thought
• Fine-tuning LLMs
• RLHF (Reinforcement Learning from Human Feedback)
• Instruction tuning

RAG & Applications

• Retrieval Augmented Generation
• Vector databases (Pinecone, Weaviate, Chroma)
• Embeddings
• Semantic search
• Knowledge bases
• Chatbots
• AI agents

Generative AI

• Diffusion models
• Image generation
• Text-to-image
• Stable Diffusion
• DALL-E
• Midjourney
• Multimodal models

Quick Reference: Top 50 Data Scientist Keywords

Check off what applies to you:

1. Python
2. SQL
3. Machine learning
4. Deep learning
5. TensorFlow
6. PyTorch
7. Scikit-learn
8. pandas
9. NumPy
10. Data visualization
11. Statistical analysis
12. A/B testing
13. AWS/GCP/Azure
14. Spark
15. NLP
16. Computer vision
17. Time series
18. Regression
19. Classification
20. Clustering
21. Neural networks
22. Transformers
23. LLMs
24. Feature engineering
25. Model deployment
26. MLOps
27. Data pipelines
28. ETL
29. BigQuery/Snowflake
30. Jupyter
31. Git
32. Docker
33. Kubernetes
34. REST APIs
35. Hypothesis testing
36. Causal inference
37. Recommendation systems
38. Forecasting
39. Data storytelling
40. Stakeholder communication
41. Cross-functional collaboration
42. Business metrics
43. Experimentation
44. Model monitoring
45. Data quality
46. Feature stores
47. Prompt engineering
48. RAG
49. Vector databases
50. Fine-tuning

Keyword Optimization Tips

Match the Job Description

Read the job post carefully. If they say "PyTorch" don't write "Torch." If they say "experimentation" don't write "A/B testing" (unless both are mentioned).

Show Context, Not Lists

Bad: "Skills: Python, TensorFlow, AWS, SQL, Tableau"

Good: "Built demand forecasting model using Python and TensorFlow, deployed on AWS SageMaker, reducing inventory costs by 15%"

Balance Technical and Business

Hiring managers want data scientists who can translate technical work to business impact. Include keywords like:

• "Increased revenue"
• "Reduced costs"
• "Improved accuracy"
• "Stakeholder presentation"
• "Business recommendation"

Prioritize Production Experience

In 2026, "I built a model" matters less than "I deployed a model that serves 1M predictions/day." Include deployment and MLOps keywords when applicable.

Next Steps

For complete formatting guidance, see our Data Scientist Resume Guide.

The best data scientist resumes don't just list keywords—they show impact. Use these keywords in context, with metrics, and you'll stand out from the hundreds of generic applications.