Types of Data Science Roles

Roles that go by the title "Data Scientist" mean different things at different companies. As a result, the interview questions you'll be asked depend on the specific role you're applying for and its focus area.

Data science roles typically into these general categories:

1. Machine learning
2. Product analytics
3. Full stack
4. Engineering

Below, we describe each type of role, provide examples of job descriptions, and highlight which topics to study for your interviews.

Machine learning

ML and data science roles often require overlapping skills, so it can be tricky to distinguish the skills and experience needed for the two.

Some data scientist roles and interviews are very similar to ML engineer roles. Many data scientists work in niche areas of NLP or deep learning.

When ML is a focus for a data science position, the interviews usually focus more on algorithms and less on software engineering.

You’ll need to know more about:

• Implementing scalable, end-to-end ML systems
• Tuning the algorithms for optimal metrics
• Differentiating between model and business performance during implementation

Below, we’ve pulled out key excerpts from sample job descriptions that fall within this category.

Airbnb: Senior Data Scientist, Algorithms, Payment

• “Hands-on develop, productionize, and operate machine learning models and pipelines at scale, including both batch and real-time use cases, structured and unstructured data.”
• “Build reusable, high-performing, scalable machine learning models with internal paved path tooling, incorporating third-party information and state-of-the-art innovations.”

NVIDIA: Senior Data Scientist, End to End Data Systems

• “Design and implement data science models, algorithms, and applications for large-scale data science projects”
• “Develop and maintain data pipelines to facilitate data collection and analysis”

Thumbtack: Staff Applied Scientist

• “Architect and deploy machine learning systems to production”
• “Expert knowledge of machine learning techniques”

TikTok: Data Scientist

• “Generalists and specialists in AI/ML techniques including computer vision (CV), natural language processing (NLP), and audio signal processing.”
• “Conduct research on the latest deep learning techniques and identify potential areas of business application.”

Product analytics

Analytics-focused data scientist roles face similar responsibilities and problems to product managers. Their responsibilities are closely tied to business metrics, using SQL and communication skills to build data stories for company leaders.

ML skills are less utilized, so you don’t need to prep as heavily for ML-focused interviews. Instead, focus on mastering these skills:

• SQL
• Product sense
• Business and product metrics
• Experimentation

Below, we’ve pulled out key excerpts from sample job descriptions that fall within this category.

Doordash: Senior Data Scientist, Analytics

• “Build full-cycle analytics experiments, reports, and dashboards using SQL, R, Python, or other scripting and statistical tools”
• “Provide insights to help business and product leaders understand marketplace dynamics, user behaviors, and long-term trends”

Meta: Data Scientist, Product Analytics

• “Use data to shape product development”
• “Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends.”

Waymo: Staff Product Data Scientist

• “Define key metrics to track the health of our commercial product”
• “Conduct various experiments to measure commercialization changes”

Full stack

Full stack data scientists utilize both ML and product knowledge. They also have a deep understanding of statistical skills such as:

• Causal inference
• Exploratory data analysis
• Statistical models
• Hypothesis testing

Below, we’ve pulled out key excerpts from sample job descriptions that fall within this category.

Walmart: Data Scientist

• “Collaborating with our data engineers to gather the right information from our various sources so that you can implement advanced statistical methodologies and design interventions to optimize how our business can best perform.”
• “Conducting exploratory analysis including hypothesis testing, statistical inference, and statistical analysis (predictive and/or prescriptive).”

Grammarly: Data Scientist, SEO

• “Design and run SEO experiments leveraging advanced statistical methodologies, including causal inference.”
• “Design and evaluate models to mathematically express and solve defined problems with limited precedent”

Google: Staff Data Scientist

• “Bring scientific rigor and statistical methods to the challenges of product creation, development and improvement”
• “Use custom data infrastructure or existing data models as appropriate, using specialized knowledge.”

Engineering

Data engineers primarily focus on preparing big data for cross-functional teams. They should be comfortable with:

• Big data technologies (e.g. Spark)
• Building batch data pipelines
• Programming languages (e.g. Scala, Python)

Below, we’ve pulled out key excerpts from sample job descriptions that fall within this category.

Netflix: Data Engineer

• “Building systems to process data efficiently and modeling the data to power analytics.”
• “Curating data across various domains such as Growth, Finance, Product, Content, and Studio.”

LinkedIn: Staff Data Engineer - Data Science

• “Design, implement, integrate and document performant systems or components for data flows or applications that power analysis at a massive scale.”

There’s often some overlap between the different types of data science roles and different ways of categorizing the roles. The smaller the company, the more broad your responsibilities as a data scientist will be. Read the job description carefully to assess which skills are most important for that particular position.