Matrix of Roles for Data Professionals
By: Kaushik Sureshkumar
Tags: Data Science, Recruitment, Data, Big Data Career

During my recent job hunt I realised that there are lots of blogs out there highlighting the differences between Data skillsets (Analyst, Data Scientist, Machine Learning Researcher etc). However, I didn’t come across many that explore how these skillsets tie in with different business functions of a company (Marketing, Product, R&D etc).

Many of the roles I came across had the same titles but the requirements and responsibilities of the roles were completely different. Making this connection between data skillset and business function not only helped me classify the different roles I discovered but also helped me understand how I wanted my career to progress. I created a framework to help with this and I wanted to share it with other budding Data Professionals that want to understand the differences between the skillsets required and the business problems that each role would focus on.

The other thing that I experienced was that there is sometimes a gap in understanding of the role between recruiters/hiring managers and applicants. This gap often isn’t highlighted until the 2nd or 3rd stage of the interview process. So I guess this post could also help hiring managers and business owners better understand what kind of Data Professional they are seeking, and in turn be able to convey that in their Role Spec, resulting in higher quality applicants and less time wasted for everyone.

To help structure the post, we’ll be using this Role Matrix:

Distribution of Data roles within an organisation (Image by Author)

This isn’t the only way to segment the Data Roles available in an organisation, it’s just the way that made the most sense to me.

We’ll be iterating through these different business functions and discussing the data roles available in each:

Growth

BI/Operations

Product

R&D

Growth

The primary focus within this business function is to grow the customer/user/client base of your company. The longer term projects that data professionals might work on within this function might include User Acquisition and Conversion Rate Optimisation among other marketing projects.

Growth Analyst

Tools — Excel, SQL, Python, Looker/Tableau, Salesforce, Analytics Tools, Digital Marketing Tools

Skills — Basic Scripting, Data Analysis, Data Visualisation, Data Story Telling, Presentation to Stakeholders/Non-Technical Audience, Analysis of A/B Tests (Desired)

Domain Knowledge — CRM Pipelines, Best Practices and Marketing Strategy for different marketing channels, Growth Specific KPIs and Metrics, Customer Segmentation, Campaign Optimisation

The day to day responsibilities of a Growth Analyst might include extracting large data sets using SQL and Python scripts and applying analysis on them to understand how effective the company’s marketing is on different channels. You will likely also be responsible for presenting on these data sets and KPIs to stakeholders and recommending changes to the marketing strategy off the back of your analysis. Apart from presentations, the responsibility of reporting automation will also tend to sit with this role.

Growth Data Scientist

Tools — SQL, Python, pandas, sklearn, keras, tensorflow, Analytics Tools

Skills — Statistical Modelling, AB Testing Design and Analysis, Causal Inference, Machine Learning, Research

Domain Knowledge — Marketing Attribution, Conversion Rate Optimisation, Campaign Optimisation, Marketing Automation

A Data Scientist working within the Growth function would typically be applying Statistical and Machine Learning techniques to optimise the growth of user base. They could be applying statistical techniques to better understand the attribution of conversions to different marketing campaigns. Similarly they could be using ML techniques to segment their user base into different groups. In some places the responsibility of deploying these models after researching and developing them also sit with this role.

Growth Data Engineer

Tools — SQL, Python, JavaScript, Cloud Platforms (AWS, Azure, GCP), Marketing Tool SDKs/APIs, Analytics Tools

Skills — Data Modelling & Architecture, ETL Pipelines, Model Deployment, DevOps, API Deployment, Web Scraping

Domain Knowledge — Reporting APIs for Marketing Tools, Growth Specific KPIs and Metrics, User Acquisition, Marketing Automation

A Growth Data Engineer will typically be responsible for acquiring, cleaning and hosting the data that Growth Analysts and Data Scientists use to solve business problems. Aside from using reporting APIs to pull useful marketing data, Growth Data Engineers would also be building and maintaining ETL pipelines that transform raw data into a more useful format. As such they’d need some back end engineering skills such as DevOps to be able to do this.

A Full Stack Growth Data Scientist is, as the name suggests, someone who can perform the responsibilities of all three of the Growth Data Roles mentioned above. It’s easy to get carried away and hire for this role but it’s important to bear in mind that while this person would be able to perform all three roles, they might not function as efficiently by themselves as if you were to hire, for example, a Growth Data Scientist and a Growth Data Engineer.

It’s also worth noting that Full Stack Data Scientists are hard to come by since most Data Professionals tend to specialise as they progress through their career. If you’re a budding Data Professional, you could choose this route rather than specialising. Bear in mind, however, that although the breadth of skills you’d develop would be large, it would be hard to delve deeper into those skills.