Different roles within data science and the skills you need for each

Insight

What does an insight data scientist do?

The insight data science team use data science to help businesses understand their world better, and to support lower-frequency, higher-impact decisions. An insight data scientist uses analysis and visualizations to create insight containing actionable recommendations, enabling customers to make more informed decisions. 

Insight data scientists typically work on shorter term projects (e.g. one month) that involve the in-depth analysis of specific trends and behaviours to answer questions that customers have. Day-to-day tasks might include:

• Scoping what insight is required by the customer
• Exploring and analysing data in R or Python to find trends and patterns
• Creating charts and visualisations to share insights in the data
• Presenting findings back to customers, focussing on actionable recommendations

What type of projects does an insight data scientist work on?

If a retailer wants to understand their performance in key trading periods, such as Black Friday, to inform their strategy, then they may engage an Insight data scientist on a project basis. This could include understanding which customers are more likely to buy and which products are more likely to sell. This insight would then be leveraged by the marketing team to plan campaigns that will appeal to customers, and by the merchandising team to plan which products they should have in stock.

What skills does an insight data scientist need?

Insight is a particularly commercial area of data science, where industry, domain knowledge and communication skills are key. The most important skills are:

• Being able to communicate well with customers; understanding the questions they have, how their business works and how insight can help
• Visualization and storytelling with data; a high attention to detail when creating charts and presentations, as well as an ability to create a compelling narrative
• Coding; ability to analyze data and create charts using R, Python or SQL

Research & development (R&D)

What does an R&D data scientist do?

R&D data scientists research cutting-edge methods and best practises to upskill the data science team, produce research outputs, and develop software tools that increase team efficiency and improve customer commercial outcomes.

In addition to fostering best practises, R&D data scientists should be subject matter experts in some specialized areas of data science, ML engineering or business domain knowledge. They often work on long-term research projects, and their daily work may include:

• Developing software packages, tools, standard apps and templates
• Researching cutting-edge algorithms and methods in their field of specialism
• Researching and benchmarking available tools for data science
• Cross-team consulting and training in best practices and areas of expertise
• Producing research outputs such as academic papers, training materials and conference talks

What type of projects does an R&D data scientist work on?

An R&D data scientist might work with the product team to turn a set of bespoke solutions that have been built for our customers into a standardized product offering, or developing a cutting-edge application that solves a business problem in a way that provides a competitive advantage to the teams that use it.

What skills does an R&D data scientist need?

Within the R&D team there are different specializations, with ML engineering at one end, research data science at the other, and a range of hybrid roles in between. This means R&D data scientists have a diverse mix of skills across a range of areas, including:

• Expertise in software and data engineering
• Mastery of machine learning and applied data science
• Python, R & SQL languages
• Deep subject-matter expertise in one or more applied fields
• Strong research, academic writing and presentation skills
• Excellent communication skills for internal consulting, teaching and conference talks

Data engineering

What does a data engineer do?

Data engineers work alongside data scientists and business teams, as part of the operations team, to ensure the reliable processing of organizational data and ensure seamless integration of outputs back into end user systems. 

Based on data scientists’ data requirements, data engineers are responsible for working collaboratively with the customer to establish where data is located and the best mechanism for transferring (or accessing) that to Peak, for both historical data transfer and then ongoing updates. Once the best approach is determined, data engineers will either implement data transfer using Peak platform functionality, or advise the customer how best to transfer or access the data.  

What type of projects does a data engineer work on?

Data engineers will liaise with other technical teams within a business to work out where data is stored, building connections or feeds to pull that data into a platform. Depending on the use case, this will be done with built-in functionality on the platform or writing bespoke tools, for example, API consumers. Once ingested, the data engineer needs to get the data into a usable state for the data science team, by exploring the data and applying any necessary transformations.

What skills does a data engineer need?

Data engineers need an understanding of how data pipelines are created technically, as well as how to ensure these are resilient through error handling and retry mechanisms. Technologies and languages they need include:

• SQL
• Python 
• APIs and HTTP response codes 

Operations

What does an operational data scientist do?

Operational data scientists build machine learning and optimization models to enable business users to make better decisions and drive favorable business outcomes – such as increasing revenues or decreasing costs. An operational data science team use data science to support businesses through higher-frequency, lower-impact decisions. 

Operational data scientists typically work on long-term projects (e.g. six months) that involve building a full solution and working closely with end users to determine how a solution will be built. Day-to-day tasks include:

• Speaking with customers to understand their processes, explaining data science outputs and gathering feedback on the project
• Attending internal project meetings to plan out work and collaborate with the customer success team
• Writing code (usually Python, R or SQL) and putting that code into production using docker, or building webapps using languages like Shiny
• Knowledge sharing with the rest of the data science team via subject-specific working groups or training sessions

What type of projects does an operational data scientist work on?

A retailer may want to know at what level they should apply reductions to their products as they come to the end of their lifecycle. This would involve an operational data scientist exploring how price impacts demand for this retailer and applying optimization algorithms to choose the best price to reduce a product while maximizing the profit made for the business. This kind of project would involve data exploration, building both machine learning and optimization models and putting models into production.

What skills does an operational data scientist have?

Operational data scientists are expected to have a range of skills, including:

• Coding: usually R, Python, and SQL
• Mathematical skills; knowledge of statistical methods/optimization techniques
• Presentation skills; as an operational data scientist often needs to speak to non-technical stakeholders, so needs to be able to explain data science clearly
• Problem formulation; often a data science problem starts as something a lot more broad, e.g. “we want to reduce our transportation costs.” A major skill of operational data scientists is being able to turn that kind of problem into a more specific problem that can be solved using data science techniques
• For more detailed information on this, see our blog on how to get a job in data science! 

Team leader

What does a data science team leader do?

Their role is to build and lead a team of data scientists, providing guidance and strategic expertise to ensure projects are successful and that the team are developing the skills required to be great data scientists.

Data science team leaders will manage a team of data scientists, providing them with support on the technical approach and management of their projects and helping their team develop skills to progress their careers in data science. Day-to-day tasks include:

• Attending project or customer meetings to offer technical guidance and ensure the project is on track to be successful
• Contribute to strategic initiatives that help to build and run a data science team, such as defining and implementing new processes
• Having one-to-ones with data scientists to help with their development, and creating a learning and development framework
• Recruiting new data scientists into the team by reviewing applications and taking part in interviews

What type of projects does a data science team leader work on?

These would include designing the recruitment process for data scientists to join the team. A recent example of this for the Peak data science team was around creating a process for our new graduate scheme. Graduates can have similar levels of skills and experience, so the recruitment process needs to be able to highlight which graduates best align with company and team values. This could involve adding questions to their initial application (as graduate CVs are very similar), a hands-on data challenge (to ensure they have the minimal technical skills required for the role), and an assessment day (as a way to evaluate many people at the same time across a variety of skills.)

What skills does a data science team leader need?

Most data science team leaders have been data scientists themselves. They need to possess these technical skills, as well as strategic and management skills to build a great team. The most important skills are:

• Experience in delivering high quality data science projects; being able to identify and mitigate risks, scope projects, create realistic timelines and guide the technical approach
• Mentoring and leadership skills to develop and support a team to perform at its very best; people skills are so important, so that your team are open with you about any challenges they’re facing and what they’d like to achieve in their careers
• A strategic mindset to further improve the data science team going forwards; you need plenty of ideas on what can be done to scale your team, increase efficiencies and build a great culture