5 Skills To Move From Junior To Senior Data Scientist
What to Expect and how to get there?
There are significant differences between a junior and a senior Data Scientist. And if you are a junior at the moment you might be wondering:
- What are the expectations for each role?
- What skills are needed to move up in the ranks?
Success is not an accident, success is a choice. — Stephen Curry
This blog post will discuss the different skills and expectations required of a junior and senior Data Scientist. We will go through:
- Key differences and expectations
- Technical skills needed and what to expect
- Why soft skills are important and which one
Key differences and expectations
A junior Data Scientist typically has around two years of experience in the field. They are usually just starting to learn new skills and hone their existing skills. On the other hand, a Senior has around five or more years of experience. As a result, they have a much deeper understanding of data science concepts and are able to apply them in innovative ways.
So, what are the different skills that junior and seniorData Scientist need? Juniors need to be competent in data cleaning, analysis, and modeling. They also need to understand basic machine learning algorithms and know how to use them effectively. Seniors need to be experts in all of these things and big data technologies and architectures. They should also be able to develop sophisticated data products and solutions.
In addition to having different skills, Juniors and Seniors also have different expectations placed on them:
- Junior Data Scientists are typically expected to carry out routine data cleaning and analysis tasks. They may also be asked to develop models or prototypes, but their primary focus is on learning new skills and expanding their knowledge base.
- On the other hand, Senior Data Scientist are expected to lead projects, mentor Juniors, and support the business stakeholders to generate ideas. They are also responsible for developing innovative solutions that solve complex business problems.
How long does it take to become a senior data scientist?
Becoming Senior takes time and dedication. It is not something that can be accomplished overnight. It typically takes around five or more years to get there. During this time, you need to learn new skills and grow your knowledge base continuously. You also need to apply the skills you have learned in real-world scenarios effectively.
What are the technical skills needed to be a Senior Data Scientist?
Being able to lead a project end to end
You need to be able to lead a Data Science project end to end. This includes data cleaning & preprocessing, feature engineering, finding the most relevant model, algorithm tuning, performance optimization, and result interpretation.
The Junior Data Scientist should already know these steps. Still, we expect a Senior to master them and execute them without external verification.
At the beginning of a project, a Senior Data Scientist should also have a clear idea of which steps will be relevant and estimate the time necessary to execute them.
This leads us to the second technical skill.
Have good project management skills
Compared to a Junior Data Scientist, a Senior will be expected to carry out most project management tasks. This will include:
- Ensuring a clear project definition: Meeting with stakeholders, writing an analytics plan, and building a detailed agenda. Defining what success looks like and setting up a tracking mechanism.
- Managing the timeline: Define what needs to be done, communicate at the right time, stopping the investigations when relevant…
- Monitoring the project execution: This includes daily/weekly status meetings, maintaining a planning, and flagging deviations from the plan.
- Communicating with stakeholders: Doing this in an effective way (not just reporting numbers). For example, being able to translate data science concepts so that business stakeholders can understand them.
Business to Data transposition: ensure impact
A Senior Data Scientist also needs to know how to transpose a business problem into a technical solution. He will need to be able to come up with hypotheses and figure out what data is required to validate them.
They should also be able to work with business stakeholders to understand how they will use the results or outcomes of the project.
This might be the essential skill of a Senior Data Scientist: making sure that the project will deliver impact.
The risk is to start a project with a mild understanding of what is at stake. Then the output might very well be impossible to use by the business stakeholder and become irrelevant.
If you can predict Y with 95% prediction, but you can only get an answer every month, this might be useless if your stakeholders need to make a decision every day. They might have been better with a less accurate daily prediction.
What are the soft skills needed to be a Senior Data Scientist?
Great communication skills arecrucial
In addition to technical skills, Senior Data Scientists also need strong soft skills. For example, they need to be able to effectively communicate with business stakeholders and convey data-related concepts in a way that they can understand.
On some occasions, strong communications skills can be less relevant if the Senior Data Scientist is going more on a technical path. Nevertheless, even the most technical Data Scientist will need to communicate its findings and be able to convince.
I have gone more into detail in this article about communication as a Data Scientist:
Coaching and leadership are required to support the team
Seniors must also learn to lead and mentor Juniors. This involves developing teamwork skills and providing guidance and support when needed. Lastly, senior data scientists must work independently and take the initiative when required.
Mentoring and training others is also a crucial aspect of a Senior Data Scientist's work. As a mentor, he must be able to:
- Provide advice on data science tools and techniques
- Guide on how to structure data analysis projects
- Review data science work products for accuracy and completeness
- Teach proper coding practices, modeling approaches, etc.
- Evaluate the potential of more Junior colleagues
Conclusion
The skills required to be a Senior Data Scientist are varied and encompass technical and soft skills:
- They must have strong analytical skills, they are also responsible for developing innovative solutions that solve complex business problems.
- They should know how to lead a project end to end.
- They must master communication with business stakeholders. See my previous article on this topic.
- They are expected to lead and mentor junior data scientists. You can read these books to help you there.
- Additionally, critical thinking skills are essential for data scientists.
Are you a Junior or Senior Data Scientist? What are your thoughts on this post? Let me know in the comments below!
Valentin Mucke
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