5 Skills to advance from Junior to Senior Data Scientist 2022

Senior Data Scientist

There are stark differences in the role of a Junior and a Senior Data Scientist. What do you think are the expectations for each role? What are the challenges at each position? And what skills do you need to become a Senior Data Scientist? Let’s explore all this.

What are the Key differences between them?

A junior Data Scientist typically has around 0-2 years of experience in the relevant field. They have just begun to learn new skills and polish their existing skills. In contrast, a Senior Data Scientist has at least 5+ years of experience in the field. 

As a result, Senior Data Scientists have a much better understanding of Data Science concepts, models and are able to effectively apply them in creative ways.

What are the different skills that junior and senior Data Scientists need? 

Juniors need to be more competent at modeling, analysis and data cleaning. They also need to have a basic understanding of machine learning algorithms and how to apply them effectively. 

Seniors need to have expertise in all Data Science related things, Big Data architectures and technologies. They should also be able to develop very sophisticated data products and solutions.

In addition to being equipped with different skills, Juniors and Seniors also have different expectations on them.

Junior Data Scientists are usually expected to have routine data cleaning and analysis based tasks. They may also be required to develop prototypes of potential models, but their primary focus should be on learning new skills and expanding their knowledge bank.

In contrast, Senior Data Scientists are expected to lead most projects, mentor their Juniors and most importantly support the shareholders to generate ideas. They are also responsible for developing innovative solutions that solve complex business problems.

How soon can you become a Senior Data Scientist?

Becoming a Senior Data Scientist takes significant time, hardwork and dedication. It is not something that can be accomplished immediately. It usually takes around 5+ years to reach that position. 

Meanwhile, you need to learn many new skills and grow your knowledge base regularly. You will also need to apply the skills you have learned in real-time scenarios in an effective manner.

What are the skills required to be a Senior Data Scientist?

Senior Data Scientist

There are mainly 5 skills that you require to become a senior Data Scientist. Let’s have a closer look at each of these skills:

1. Lead a project from start to finish

The first thing as a skill is that you need to be capable of leading a Data Science project right from the start to the end of it. Data cleaning and pre-processing, algorithm tuning, feature engineering, result interpretation, finding the most relevant model and performance optimization.

As a junior you should already know these steps. Yet, companies expect a Senior to master them and execute them without a need for review.

At the beginning of a project, a Senior should also have a clear idea of each step that will be relevant and accurately estimate the time required to execute them.

2. Project Management skills

Compared to a Junior Data Scientist, a Senior will be expected to manage all tasks related to the project. They have to:

  • Ensure a clear project definition. Meeting with stakeholders, writing an analytics plan and building a detailed agenda is important. Defining what success looks like and setting up a tracking mechanism helps the stakeholders understand the project better.
  • Communicating with stakeholders. Presentation in an effective way with less use of numbers. Being able to translate Data Science concepts to ensure better understanding for the business stakeholders.
  • Manage the timeline. Define what requires to be done, always communicate at the right time and stop the investigations when required.
  • Monitor the project execution. This includes daily or weekly status meetings, maintaining a planner and flagging alterations from the plan.

3. Ensuring impact with Data transfer

A Senior Data Scientist should know how to transfer a business problem with a technical solution. Being able to come up with hypotheses and figuring out what data is required to validate them is important.

To make sure that the project will deliver an impact is an essential part of being the Senior Data Scientist. It is best to be sure and then begin a project, rather than having half the idea of what is at stake and delivering an irrelevant output.

4. Communication skills

A Senior Data Scientist should also have great soft skills. They need to be able to communicate everything to the business shareholders in the most effective manner, while keeping in mind that it needs to be relevant to their understanding.

Even while providing numbers and data, visual aids and a to the point conversation will aid to your ability to convince your findings to the shareholders.

5. Leadership and Coaching

As a Senior Data Scientist, you must possess the ability to lead and coach effectively. Be a good mentor to your juniors. Make sure to develop teamwork skills and provide guidance and support when required. 

A Senior Data Scientist should also be able to take charge and lead certain projects independently as and when required.

You should also be able to:

  • Provide advice on relevant Data Science tools and techniques.
  • Review Data Science work products for accuracy and completeness.
  • Guide on how to structure data analysis projects.
  • Evaluate the potential of the Junior colleagues
  • Teach and implement proper coding practices, modeling approaches, etc.

In conclusion, it is not that easy to be a Senior Data Scientist but it is very achievable to be one. Hopefully this blog helped you get a better insight into how one can become a Senior Data Scientist.

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