We spoke with Niall Wharton, Team Lead in Data Science at Xcede, to find out more about what being a Senior Data Scientist involves and how to know if you’re ready for the jump.
Thanks for talking with us, Niall. To kick us off, can you describe the primary difference between a Senior and Junior Data Scientist?
The structure of a Data Science department varies significantly between countries and companies, especially because the market is still in its relative infancy.
However, for most, the fundamental difference between a Senior and Junior Data Scientist is the amount of responsibility that an individual is given. Senior Scientists usually have an increased level of which allows them to manage and deliver projects successfully, as explained by Guillermo Barquero, Data Science Manager at Tesco:
“I'd say the main difference is around project management: being able to efficiently translate business problems into mathematical ones and drive the technical part of the project: including selecting the right approach for the specific use case and managing stakeholders.”
What can a Senior Scientist expect to do as part of their role?
Again, each company is unique, but normally a Senior Data Scientist owns the entirety of a data project. This includes the glamorous model building and machine learning stages and the not-so-glamorous data collection, data cleaning and productisation stages.
Mahana Mansfield, Data Science Director at Deliveroo, describes the scope of a Senior Data Scientist’s role well:
“For me, a Senior Data Scientist is someone who can own a project end-to-end, right from formulating the problem, through developing a solution (potentially mentoring more Junior Data Scientists to rolling out and iterating, and knowing when to move on to something more impactful.”
When is someone ready to become a Senior Data Scientist?
While you might think readiness for a senior position is based on longevity or technical ability, it’s actually more often based on relevant or impactful experience.
When someone can show that they’ve helped deliver projects that add real value to an organisation (for example, customer purchase uplift or improved results in any domain), that’s a strong sign they’re ready for the next level. Technical ability is great, but employers want to see facts and experience to back up that expertise.
Does that mean senior promotions are more results-driven than skill-driven?
In most cases, yes. It’s a common misconception that just because someone is doing something more technically complex or has been in their position for a long time, that they deserve senior status. In reality, someone who has built something simple but that adds genuine value to a business is often more suited to a senior position if that was the best way to get great results.
We often find that leading Data Scientists say they know someone has matured as a Data Scientist when they look for the simplest solution first - one that doesn’t necessarily use machine learning. We’re definitely seeing candidates with a portfolio of results win jobs over candidates with a portfolio of skills.
How important are Data Scientists to organisations and where do they fit into the day-to-day running of a department?
This is a great question, and I’d be interested in hearing our readers’ response to this one. For smaller companies and start-ups, I think Senior Scientists add genuine value when it comes to mentoring and working with other teams. The Head of Data Science is often drawn into more exec-level work, allowing Senior Data Scientists the opportunity to mentor Junior Scientists and coach others, as described by Chanuki Illushka Seresinhe, Lead Data Scientist at Popsa:
“When I judge whether a Data Scientist is ready to be a Senior Data Scientist, I look for what they are doing beyond their technical competence. Exceptional Senior Data Scientists start to recognise that in order to do their jobs to the highest degree they also need to take their own initiative and start learning wider tasks beyond data science. Can they bridge gaps between data science and other teams? Can they communicate complex data science goals to non-technical audiences? Can they mentor people in a way that encourages them to grow?
“While many people focus on achieving technical mastery in Data Science, only a few people recognise the importance of these other crucial skills.”
For larger companies, Senior Scientists add value in terms of experience and project delivery, as Roshini Johri, Principal Data Scientist at HSBC, explains:
“In my opinion a Senior Data Scientist is someone who has had experience in running end to end machine learning pipelines including deployment to production as well as stakeholder engagement.
“I would definitely not consider someone as a senior if they haven't at least worked 2-3 years as a Data Scientist …[and]...I see a lot of companies make this mistake and bring on people with no experience in the relevant field as seniors.”
If you’re looking for a Senior Data Scientist job role or a hire to join your team, get in touch with our expert data science and machine learning consultants to find out how we can help.