Data Science Roles – A Definitive Guide

Jeff discussed some of these and other data science roles at the 2019 Open Data Science Conference

8 Key Data Science Roles

There are countless different data science roles and team structures, and many organizations use different titles than these eight. Moreover, a role does not necessarily map to a specific person. Rather, the same person often serves multiple roles. Regardless, these eight roles are typical for a fully staffed data science team.

Data Scientist

The most obvious key role in a data science team is that of the data scientist. A data scientist is inherently very curious – trying to understand certain phenomena through the analysis of modeling of complex data.

Among all the team roles, the data scientist tends to be the strongest in statistics, math, and machine learning. They should also have a strong foundation in programming – typically in Python or R. Often they start their careers or studies in math or a quantitative research-oriented such as economics or physics. The role generally is not entry-level and might require an advanced degree and a few years of experience.

Typical Responsibilities:

• Develop statistical models, machine learning algorithms, and predictive analytics solutions to address business challenges.
• Analyze large amounts of complex data to extract insights and drive decision-making.
• Design experiments to test hypotheses and measure the effectiveness of solutions.
• Collaborate with data engineers and data analysts to collect and preprocess data, and build and maintain data pipelines.
• Use data visualization tools to communicate insights and findings to stakeholders.

Typical Qualifications:

• Bachelor’s or Master’s degree in math, stats, computer science, data science, or related quantitative field.
• Strong programming skills in Python or R.
• Strong SQL skills and understanding of databases.
• Strong experience with machine learning algorithms and libraries such as scikit-learn, TensorFlow, or PyTorch.
• Familiarity with data visualization tools such as Tableau, Power BI, or matplotlib.
• Strong analytical and problem-solving skills, with the ability to work with complex and unstructured data.
• Strong communication skills and ability to work collaboratively with cross-functional teams.

Data Engineer

Without good data – you can’t get very far in data science. Thus, perhaps the second most key role is that of data engineer who is responsible for the collection, storage, and processing of data.

They design, build, and maintain the infrastructure that enables the data science team to work with large amounts of data. This includes databases, data pipelines, and data warehousing solutions. The data engineer ensures that data is available when and where it is needed, and that it is of high quality. Some organizations have their data engineers sit in a separate team from the data scientists.

Typical Responsibilities:

• Design, build, and maintain data pipelines to move and transform data from various sources into a target location such as a data warehouse or data lake.
• Develop and maintain the infrastructure required to support data science initiatives, including data warehousing, ETL or ELT tools, and data integration solutions.
• Ensure data quality, accuracy, and consistency across multiple data sources.
• Work with data scientists, data analysts, and other stakeholders to understand data requirements and provide support for data-driven decision-making.

Typical Qualifications:

• Bachelor’s or Master’s degree in computer science, data science, or a related field.
• Strong programming skills in one or more languages such as Python, Java, or Scala.
• Strong experience with SQL, NoSQL, and data warehousing technologies such as Redshift, Snowflake, or BigQuery.
• Experience with ETL tools such as Apache Airflow, AWS Glue, or Azure Data Factory.
• Familiarity with distributed computing frameworks such as Hadoop, Spark, or Flink.
• Knowledge of data modeling, data integration, and data quality concepts.
• Strong communication skills and ability to work collaboratively with cross-functional teams.

Data Analyst

A data analyst is a professional who is responsible for collecting, processing, and performing statistical analyses on large sets of data. They use various analytical tools and techniques to extract meaningful insights from data and communicate those insights to decision-makers. Often this role overlaps with similar functions such as business analysts who lead requirements gathering and understand business needs and business intelligence analysts who build dashboards for stakeholder consumption.

This role is similar to a data scientist but data analysts tend to be more focused on reporting on the current state as opposed to predictive analytics. Knowing Python and R helps but data analysts are generally not as skilled in programming as data scientists. For a deeper understanding of the differences between these roles, read Data Analyst vs Data Scientist. Many organizations embed data analysts within business function teams.

Typical Responsibilities:

• Collect and preprocess data from multiple sources to ensure data quality, accuracy, and consistency.
• Analyze and interpret complex data to identify patterns and trends, and to provide insights that support business decision-making.
• Develop dashboards and reports using data visualization tools to communicate insights and findings to stakeholders.
• Collaborate with data scientists and data engineers to collect and preprocess data, and build and maintain data pipelines.

Typical Qualifications:

• Bachelor’s or Master’s degree in computer science, business analytics, or a related field.
• Strong proficiency in SQL and data visualization tools such as Tableau, Power BI, or QlikView.
• Experience with statistical analysis and A/B testing methodologies.
• Familiarity with data modeling and data preprocessing techniques.
• Strong analytical and problem-solving skills, with the ability to work with complex and unstructured data.
• Strong communication skills and ability to work collaboratively with cross-functional teams.

Machine Learning Engineer

This role blends the world of software (and typically cloud) engineering and data science. The machine learning engineer is responsible for building and deploying machine learning models. They work closely with the data scientist to determine the best algorithms and models to use, and they build and implement these models in a production environment. The machine learning engineer is also responsible for monitoring the performance of models and making updates and improvements as necessary. 

Relative to the data scientist, the machine learning engineer tends to be weaker is math/stats but stronger in writing production code and maintaining production systems.

Sometimes, this role sits outside of the data science team on a software or data engineering team.

Typical Responsibilities:

• Design, develop, and deploy scalable machine learning models and systems that support business objectives.
• Collaborate with data scientists and data engineers to collect and preprocess data, and build and maintain data pipelines.
• Develop and maintain data infrastructures that support machine learning workflows, including data storage, feature engineering, and model training.
• Design and implement distributed systems that support large-scale machine learning.
• Develop and maintain machine learning workflows that are efficient, reproducible, and scalable.
• Implement monitoring and evaluation systems that track model performance and identify potential issues.

Typical Qualifications:

• Bachelor’s or Master’s degree in computer science, engineering, or a related field.
• Strong programming skills in one or more languages such as Python, Java, or C++.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience with distributed systems such as Apache Spark, Hadoop, or Kafka.
• Familiarity with data storage and processing technologies such as SQL, NoSQL, and Apache Beam.
• Strong analytical and problem-solving skills, with the ability to work with complex and unstructured data.
• Strong communication skills and ability to work collaboratively with cross-functional teams.

Product Owner

Many data science teams struggle to understand how they can effectively drive value for the broader organization. The key role to overcome this challenge is the product owner (often termed as a product manager).

This role is responsible for setting the product vision, defining product requirements, and prioritizing the product backlog. They work with stakeholders to understand business requirements and ensure that the data science team is delivering value to the organization. The best product owners are excellent story-tellers who can communicate their compelling vision.

To learn more, read the Data Science Product Manager post.

Typical Responsibilities:

• Define and prioritize product requirements that support business objectives, based on customer needs, data insights, and market trends.
• Work with cross-functional teams, including data scientists, data analysts, data engineers, and software developers, to develop and deliver data-driven products that meet customer needs.
• Communicate product requirements and progress to stakeholders, including senior leadership, customers, and cross-functional teams.
• Develop and maintain product roadmaps that align with business objectives and account for technical feasibility and resource constraints.
• Verify that solutions delivered serve their intended purpose and often train stakeholders to understand and use the solutions.

Typical Qualifications:

• Bachelor’s or Master’s degree in a business or informatics field.
• Experience with Agile coordination frameworks, including Scrum, Kanban, and Data Driven Scrum.
• Familiarity with data science concepts and methodologies, including statistical analysis, machine learning, and data visualization.
• Excellent communication skills, with the ability to effectively communicate technical concepts to both technical and non-technical stakeholders.
• Strong experience in office productivity tools (such as Jira, Asana), flow diagram tools, and prototyping tools (like Sketch or Fimga).
• Strong domain knowledge (or the ability to quickly to learn a new business).
• Familiarity with data governance and regulatory compliance requirements.

Process Expert

The process expert is responsible for ensuring that the team is working effectively together and with broader stakeholders. They help team members understand and adopt effective Agile principles and collaboration frameworks such as Scrum, Kanban, or Data Driven Scrum. They facilitate communication, remove impediments, and help the team to continuously improve its processes.

This role has different titles including agile coach, process expert (a Data Driven Scrum role), or Scrum master (a Scrum role). Some organizations split a process expert’s allocation across multiple teams.

Typical Responsibilities:

• Coach and mentor the team on agile principles, processes, and practices, and help the team continuously improve.
• Facilitate agile ceremonies, including sprint planning, daily stand-ups, sprint reviews, and retrospectives.
• Work with the product owner to ensure that the product backlog is prioritized and refined, and that it aligns with business objectives.
• Facilitate communication and collaboration within the team and with stakeholders, and remove impediments that prevent the team from achieving its goals.
• Identify and escalate risks and issues that impact the team’s ability to deliver on time and with quality.

Typical Qualifications:

• Bachelor’s or Master’s degree in business, computer science, engineering, or a related field.
• Strong understanding of Agile principles and frameworks including Scrum, Data Driven Scrum, and Kanban.
• Excellent communication and facilitation skills, with the ability to communicate effectively with both technical and non-technical stakeholders.
• Strong problem-solving skills, with the ability to identify and remove impediments that prevent the team from achieving its goals.
• Strong leadership and coaching skills, with the ability to coach and mentor the team on Agile practices and principles.
• Familiarity with data science concepts and methodologies, including statistical analysis, machine learning, and data visualization.

Project Manager

Many organizations struggle to apply effective project management practices to data science. To overcome these challenges, a project manager in data science can drive project success by applying the right project approaches that cater to the unique aspects of data science. The data science project manager will work closely with cross-functional teams, including data scientists, analysts, engineers, product managers, and stakeholders, to ensure successful project execution.

This role most closely resembles that of a process master, and many teams have the same person serve as both project manager and process master. Other teams might rely on a lead data scientist to serve as a project manager for a specific project.

To learn more, read the Data Science Project Manager post.

Typical Responsibilities

• Develop and implement data science project plans, ensuring that projects are completed on time, within budget, and to quality standards.
• Coordinate and monitor day-to-day tasks and workflows of the project team.
• Manage stakeholder requests and expectations; provide updates to project sponsors.
• Scope and define tasks that fulfill the project vision; manage and document scope using a project management ticketing system such as Jira, Atlassian, or Rally.
• Manage contracts with vendors and suppliers.
• Manage the sourcing of data sets required for upcoming and current projects.

Typical Qualifications:

• Bachelor’s or Master’s degree in business, computer science, statistics, mathematics, or a related field.
• Strong understanding of the data science project life cycle.
• Strong understanding of Agile approaches, including Scrum, Data Driven Scrum, and Kanban.
• Excellent communication, interpersonal, and leadership skills, with the ability to influence and motivate cross-functional teams.
• Strong problem-solving, analytical, and critical thinking skills, with the ability to make data-driven decisions.
• Strong experience in office productivity tools (such as Jira, Asana), flow diagram tools, and prototyping tools (like Sketch or Fimga).
• Ability to manage budgets, scope, and schedules.

Team Manager

The team manager is responsible for overseeing the data science team, ensuring that the team is meeting its goals, and managing individual team member’s performance. They lead recruitment, performance management, training, and often administrative responsibilities such as vendor management. They work with stakeholders to ensure that the data science team is delivering value to the organization and aligning with the organization’s strategic goals.

The team manager typically supervises the data scientists, analysts, and engineers. Sometimes the process master and product owner also report to the team manager but often these roles report through separate org structures like a PMO or product team.

To learn more, read the 6 Actions be a better Data Science Manager post.

Typical Responsibilities:

• Lead the data science team, providing guidance, direction, and mentorship to team members.
• Collaborate with other teams and stakeholders to identify data science opportunities that align with business objectives.
• Manage the team’s resources, including budget, personnel, and equipment, and ensure that resources are used efficiently and effectively.
• Develop and maintain relationships with key stakeholders, including business partners, customers, and vendors.
• Monitor and report on the team’s performance, including progress against goals, budget, and project milestones.

Typical Qualifications:

• Bachelor’s or Master’s degree in computer science, engineering, statistics, or a related field.
• Prior experience as data scientist, product manager, or as a software manager.
• Excellent project management skills, with the ability to develop and implement project plans that meet business objectives.
• Strong leadership and communication skills, with the ability to motivate and mentor team members and collaborate with stakeholders.
• Excellent problem-solving skills, with the ability to identify and mitigate risks and issues that impact project delivery.
• Familiarity with data science tools and technologies, such as Python, R, SQL, and Hadoop.

Data Science Role’s Focus during a Project

A member on a data science team should be somewhat involved in all aspects of a data science project. This helps:

• Keep the team members focused on the broader objective (as opposed to a sub-phase of a project).
• Reduce bottlenecks in case of surge in demand for a certain phase of project work or decrease in staff (e.g. a team member is out-of-office or resigns).
• Reduce the quantity and impact of any hand-offs.
• Broaden each team member’s skills beyond their core strength.

However, you shouldn’t expect team members to be at their strongest in every single aspect of a project. Rather, team members will have specific strengths which align their relative focus to certain phases of a data science project.

The table below shows six of the key positions in each row and the six phases of a data science project (as defined by CRISP-DM). The relative focus that each role might play in each life cycle phase appears in the grid.

Relative focus of each role during each project phase

Note the process master and team manager don’t appear in this table because they generally don’t align their time toward specific aspects of the project life cycle. Rather, they focus on improving the overall team process and team function.

Data Science Role Opportunities

Data science career paths can be rewarding and lucrative. And the demand continues to grow with a recent publication seeing a 295% increase in the number of data science-related tasks recruiters set up for candidates in 2021 (devskiller.com).

Indeed, seven of the eight roles in this post appear in Glassdoor’s Top 50 jobs in the United States for 2022 report. Note this looks at the generalized roles and not specifically for those on a data science team.

Source: Glassdoor, 2022

Conclusion

The most important take-away is to understand that there is a lot more to a data science team than just a data scientist. Rather, there are numerous data science roles that work together to successfully deliver data science projects.