Data scientist is a job title that covers many different roles. An AI engineer and a statistical scientist might both use it, but what they actually do every day looks nothing alike.
That matters if you’re trying to break into the field or pick a direction. Each type of data scientist uses different tools, works on different problems, and follows a different career path. Understanding those differences makes it a lot easier to figure out where you fit.
Key Points
- Data science roles include many distinct specializations, from data analysts and data engineers to AI engineers and AI/machine learning research scientists.
- Each role calls for different skills and workflows, so the right path depends on what you like to build or research.
- Getting a sense for the different types of data scientists can help you choose where to specialize or what kind of data talent to hire.
- For broader career guidance, Intuit’s guide on how to become a data scientist can help you map your next step.
- The US Bureau of Labor Statistics reports that data scientists earn a median annual wage of $112,590.
1. Data Analyst
Becoming a data analyst is how many people break into the field of data science. Data analysts clean and analyze structured data to uncover trends and answer business questions. They often turn raw data into dashboards and visualizations that nontechnical stakeholders can quickly understand and act on.
Common tools include:
- Excel for quick data cleaning, pivot-table analysis, and trend exploration
- SQL for querying databases and pulling the right data for analysis
- Tableau for building interactive dashboards and visualizations
- Power BI for creating key performance indicator (KPI) reports and dashboards connected to business systems
- Python for automating analysis and running deeper statistical analysis
Example: A retail analyst might dig into customer behavior to pinpoint exactly where users abandon a sign-up flow.
Related reading: Learn more about the difference between a data analyst and a data engineer.
2. Machine Learning (ML) Engineer
A machine learning engineer bridges data science and software engineering. Among data scientist roles, this specialization focuses on building and deploying ML models at scale into production systems. While accuracy’s a big deal, machine learning engineers also make sure models are fast and secure.
Common tools ML engineers use include:
- Python for building and training ML models
- TensorFlow and PyTorch for deep learning frameworks and neural network development
- scikit-learn for classical machine learning algorithms and model evaluation
- Docker for containerizing models and keeping environments consistent across teams
- Kubernetes for deploying and scaling models in production
Example: A machine learning engineer at a fintech company might build and deploy a fraud detection model that scores transactions in real time, helping protect customers while keeping digital experiences moving smoothly.
3. Data Engineer
A data engineer builds and maintains the data infrastructure that other teams rely on. In many types of data science, data engineers make the work possible by designing pipelines that move information from source systems into data warehouses, data lakes, and analytics platforms. Above all, data engineers focus on producing clean data that’s reliable and secure.
A data engineer’s toolkit often includes:
- SQL for querying, transforming, and validating data across databases
- Python for building data pipelines, automating workflows, and processing large datasets
- Spark for distributed data processing across massive datasets
- Airflow for scheduling, monitoring, and managing data pipeline workflows
- dbt for transforming raw data into clean, tested, analytics-ready datasets
- Snowflake for storing, managing, and querying cloud-based data warehouse tables
- BigQuery for analyzing large datasets in Google Cloud
- Amazon Web Services (AWS) or Azure for building, deploying, and managing cloud data infrastructure
Example: A data engineer might build a pipeline that updates customer transaction data every hour for reporting and ML models.
4. Business Intelligence (BI) Analyst
A business intelligence analyst turns data into strategic insights that leaders use to make confident decisions. Compared with some data science types, this role is more business-facing. That means a stronger focus on stakeholder communication and data-driven storytelling.
More specifically, business intelligence analysts build dashboards and monitor KPIs to identify trends that reveal what’s working and what needs attention.
BI analysts use tools like:
- SQL for querying databases and pulling the right data for reporting
- Tableau for building interactive dashboards and data visualizations
- Power BI for creating KPI reports and business performance dashboards
- Looker for exploring governed business data and sharing insights across teams
- Excel for quick analysis, data checks, and stakeholder-ready summaries
- Snowflake or BigQuery for storing and analyzing large business datasets
Example: A BI analyst might create an executive dashboard that tracks revenue, customer growth, and retention by market.
5. Statistical Scientist
A statistical scientist applies rigorous statistical methods to answer complex questions with confidence. Among the types of data scientists, this role is especially important in fields where accuracy and validity matter, such as pharma and scientific research. Statistical scientists design experiments and analyze clinical or operational data. They then explain what those results do (or don’t) prove.
Common tools include:
- R for statistical modeling, hypothesis testing, and experiment analysis
- Python for data analysis, statistical workflows, and automation
- SAS for regulated statistical analysis, especially in healthcare and pharma
- SPSS for survey analysis, social science research, and statistical reporting
- SQL for pulling structured data from databases for analysis
- Statistical modeling libraries for regression, forecasting, probability modeling, and uncertainty analysis
Example: A statistical scientist in healthcare might evaluate whether a new treatment improves patient outcomes while controlling for bias, sample size, and risk.
6. Natural Language Processing (NLP) Scientist
A natural language processing scientist specializes in helping machines process and generate human language. This data science role is particularly relevant with the rise of large language models (LLMs) and generative AI.
NLP scientists build systems for text classification, sentiment analysis, chatbots, search, translation, summarization, and language generation.
NLP scientist tools include:
- Python for building workflows, training models, and automating text analysis
- PyTorch for developing and fine-tuning deep learning models for language tasks
- TensorFlow for training, deploying, and scaling models
- spaCy for text preprocessing, named entity recognition, and production-ready NLP pipelines
- Hugging Face for working with transformer models, LLMs, tokenizers, and pretrained language models
- NLTK for foundational tasks like tokenization, stemming, tagging, and text classification
- Transformer-based models for tasks like summarization, translation, sentiment analysis, chatbots, and language generation
Example: An NLP scientist might build a customer support chatbot that understands user intent and improves over time based on real conversations.
7. Computer Vision Scientist
A computer vision scientist develops models that interpret and analyze visual data, including videos and 3D scans. Within data scientist roles, this specialization is common in robotics, healthcare, manufacturing, retail, security, and autonomous systems. Computer vision scientists work on tasks like object detection and autonomous navigation.
Common tools include:
- Python for building computer vision workflows, training models, and processing image or video data
- OpenCV for image processing, object detection, feature extraction, and video analysis
- TensorFlow for building, training, and deploying computer vision models at scale
- PyTorch for developing and fine-tuning deep learning models for image and video tasks
- Keras for quickly prototyping neural networks and image classification models
- Cloud vision APIs for adding prebuilt image recognition, object character recognition (OCR), object detection, and labeling capabilities to applications
Example: A computer vision scientist at an autonomous vehicle company might build models that detect pedestrians and lane markings in real time.
8. AI/ML Research Scientist
An AI/ML research scientist pushes the boundaries of what’s possible with artificial intelligence and machine learning. Across advanced types of data science, this role focuses on developing new algorithms and model architectures that improve how AI systems reason and perform. Research scientists often run experiments and prototype novel approaches before they become production-ready tools.
An AI/ML research scientist’s toolset might include:
- Python for running experiments, building prototypes, and developing machine learning workflows
- PyTorch for designing, training, and testing deep learning models and new model architectures
- TensorFlow for building scalable machine learning models and experimenting with neural networks
- JAX for high-performance numerical computing and advanced machine learning research
- NumPy for data manipulation and experimental model development
- Cloud computing platforms for running large-scale experiments and managing compute-heavy workloads
Example: An AI/ML research scientist might design a new model architecture that improves recommendation accuracy or makes AI systems more efficient.
9. Analytics Engineer
An analytics engineer sits between data engineering and data analysis. Among data science roles, analytics engineers turn raw data into clean, tested datasets that analysts and scientists can trust. They often own the data modeling layer and define business logic with the goal of using consistent metrics across dashboards and models.
Common tools include:
- SQL for transforming raw data into clean, analysis-ready datasets
- dbt for building, testing, documenting, and version-controlling data models
- Git for tracking changes and collaborating with teammates
- Snowflake for storing, transforming, and querying cloud-based data warehouse tables
- BigQuery for analyzing large datasets and powering analytics workflows in Google Cloud
- Looker for creating governed dashboards, defining shared metrics, and helping teams explore trusted data
- Data quality testing tools for checking accuracy and consistency across datasets
Example: An analytics engineer might create a reliable revenue model that powers executive reporting and forecasting.
Related reading: Learn more about data scientist vs. engineer roles.
10. AI Engineer
An AI engineer is focused on building production applications powered by LLMs. It’s a role that’s more product-focused than research-focused. Instead of developing new models from scratch, AI engineers integrate foundation models like GPT, Claude, and Gemini into real-world tools using prompt engineering, retrieval-augmented generation (RAG), agentic workflows, and evaluation frameworks.
AI engineers use tools like:
- Python for building AI applications and connecting APIs
- LangChain for creating LLM applications with chains, agents, tools, memory, and retrieval-augmented generation
- LlamaIndex for connecting LLMs to private data sources, documents, and knowledge bases
- Vector databases for storing and searching embeddings that help AI systems retrieve relevant information
- Pinecone for powering semantic search and retrieval-augmented generation at scale
- Weaviate for building vector search, hybrid search, and AI-native data retrieval systems
- LLM APIs for integrating foundation models like GPT, Claude, and Gemini into production applications
- Prompt engineering tools for refining and managing prompts that guide model behavior
- Evaluation frameworks for measuring safety and reliability before AI features ship
Example: An AI engineer at a fintech company might build a customer support agent that uses RAG over help docs to answer billing questions.
How to Choose the Right Data Science Role
Choosing between data science types starts with what energizes you most.
- If you love coding and systems, consider data engineering or machine learning engineering.
- If you’re drawn to business strategy, business intelligence or decision science may be a better fit.
- If you enjoy math, experimentation, and research, explore AI/ML research.
- If you want to build with foundation models and ship AI products, look at AI engineering.
- And if language or visual data excites you, NLP or computer vision could be your path.
Many data scientists move between specializations as their careers grow. Becoming a data scientist of any particular type doesn’t bind you to that career path.
| Types of Data Scientists |
| Role | Primary focus | Key tools | Typical entry path |
| Data analyst | Trends, dashboards, and reporting | SQL, Excel, Tableau, Power BI, Python | Entry-level analyst role, bootcamp, or degree |
| Machine learning engineer | Building and deploying ML models | Python, TensorFlow, PyTorch, Docker, Kubernetes | Software engineering or data science background |
| Data engineer | Data pipelines and infrastructure | SQL, Python, Spark, Airflow, Snowflake | Software, analytics, or database experience |
| Business intelligence analyst | KPIs and strategic business insights | SQL, Tableau, Power BI, Looker, Excel | Business analyst or data analyst role |
| Statistical scientist | Experiments, hypotheses, and uncertainty | R, Python, SAS, SPSS, SQL | Statistics, math, or research background |
| NLP scientist | Human language systems | Python, Hugging Face, spaCy, PyTorch, TensorFlow | ML, linguistics, or computer science path |
| Computer vision scientist | Image, video, and visual data analysis | Python, OpenCV, PyTorch, TensorFlow, Keras | ML, robotics, or computer science path |
| AI/ML research scientist | New algorithms and model techniques | Python, PyTorch, TensorFlow, JAX, NumPy | Advanced degree or research experience |
| Analytics engineer | Clean, tested, trusted datasets | SQL, dbt, Git, Snowflake, BigQuery | Data analyst or data engineering path |
| AI engineer | LLM-powered production applications | Python, LangChain, LlamaIndex, vector databases, LLM APIs | Software engineering, ML, or product-focused data role |
Explore Data Science at Intuit
The types of data scientists at Intuit work across models, pipelines, AI, analytics, and insights that power products serving more than 100 million customers worldwide. For data scientists who want to build with purpose and solve real customer problems at scale, explore data science jobs at Intuit today.
FAQs
How can networking help in data science careers?
Networking can help you learn what different data science roles look like in practice, discover job opportunities, and get advice from people already working in the field. One way to connect with mentors and recruiters is through industry events or online communities.
How important are certifications in data science?
Certifications can help show initiative and build foundational skills, especially if you’re changing careers or starting out. But they’re usually strongest when paired with hands-on projects and real experience using tools like machine learning libraries or visualization platforms.
What projects can showcase my skills in data science?
Strong data science projects solve a clear problem and show your process from data cleaning to insights or model results. Examples include a customer churn model, sales forecast, fraud detection prototype, NLP sentiment analysis, computer vision classifier, or interactive dashboard with documented business recommendations.