I – Hands-On Data to Data Science or Data Engineering Leader

Introduction

Congratulations on choosing data science as your next career step! It’s a great decision.

Data science is a thriving field with a remarkable number of job openings around the globe. The demand is outstripping the supply! That means there are more vacancies than qualified data science professionals.

So this journey you have taken to become a data science leader? You can already visualize why it’s the path to future success. There are a variety of problems you can solve, a whole host of tools you can master, and a broad range of techniques you can learn and then play around with.

The canvas is in front of you – now it’s your turn to pick up the data science brush and start painting your way to a successful data science transition. And the fact that you bring years of experience to the table is a huge bonus – if you know how to leverage that!

What can you expect in data science or a data engineering leadership role?

Data science may be the sexiest job of the 21st century but like all jobs, even this one requires hard work. A day-to-day hands-on role in data science requires working on the same problem for long hours performing continuous in-depth research. And when you are aiming for leadership, you’ll need your experience to shine out.

A data science leadership role will require you to put on different hats throughout your day. You would be in continuous communication with the stakeholders as well as other teams. You’d want to keep up on your communication skills, storytelling skills, and structured thinking ability. And to add complexity to the matter, you’ll need to marry these soft skills with your data science knowledge! We’ll talk about these skills in a moment.

You’ll be jumping from a meeting about client requirements to one about how your data science team needs help on a certain solution and then to another meeting about how your data engineering team is struggling with make the components fit in a machine learning pipeline. Do you see where the different hats will come from?

Here’s what a typical data science project lifecycle looks like this:

• Converting the business problem into a data problem
• Hypothesis generation
• Data collection or extraction
• Data exploration and validating hypotheses
• Data modeling
• Model deployment
• Presenting your work to the final user/client/stakeholder

As a data science leader, you would be involved at a high level of building machine learning solutions. While you would not be required to know the derivations of machine learning algorithms, you would be expected to understand how an algorithm works (more on that soon).

What are the key skills required to excel in a data science leadership role?

Data science is a multi-faceted role. There is no one-size-fits-all approach to learning data science. Having said that, there are a few core skills you will need to pick up to make a successful career transition to data science.

Since you bring a lot of experience to the table from your hands-on background (as opposed to freshers transitioning into data science), your skillset would vary depending on your level, your domain and your previous experience.

Here are the key skills you would need from a data science perspective:

• Knowledge about AI and ML techniques
• Exposure to the end-to-end machine learning pipeline
• Explore to cloud computing
• Data engineering
• Problem Formulation Ability
• Storytelling and communication
• Structured thinking

How can you excel in each of these required skills?

Ah, the key question! Now that you know what you need to learn, the attention turns to how you can learn those skills. Let’s look at a few options and suggestions on how to pick up and hone the key skills we mentioned above.

AI/ ML Techniques

For a data scientist, machine learning is the core skill to have. For a data science leader, you need to at least be comfortable with the different techniques at play and how they work.

Machine learning is used to build predictive models. For example, you want to predict the number of customers you will have in the next month by looking at the past month’s data, you will need to use machine learning algorithms.

You can start with a simple linear and logistic regression model and then move ahead to advanced ensemble models like Random Forest, XGBoost, CatBoost, and so on. It’s a good thing to know the code for these algorithms (which just takes 2-3 lines) but what’s most important is to know how they work. This will help you in hyperparameter tuning and ultimately a model that gives a low error rate.

If you are looking for specialization, Natural Language Processing (NLP) and Computer Vision are two fields that are absolutely thriving right now. Each requires you to dive deep into those specific fields so make sure you’re aware of what you’re getting into.

Needless to say, having hands-on experience with these machine learning algorithms will add a lot of weightage to your transition. As a data science leader, you would be required to take ambiguous business problems and convert them to data or machine learning problems.

Problem Formulation

As a data science leader, you’ll be spending majority of time formulating business problem from unstructured requirements.

As organizations move to adopt data science in their organization structure, they need someone to turn their unclear requirements into clearcut problem statements.

For example, an E-commerce company wants to increase its user retention. Well, that’s a very uninformative and unstructured problem. A better problem statement maybe – To increase user retention by 20% on the product catalog page by incorporating state-of-the-art recommendation engines.

Problem formulation is one of the most important skills that a data science leader possesses which sets the tone for the entire timeline of the project.

Building Machine Learning Solutions

A data science leader must be familiar with all the machine learning algorithms, the best practices, and experience in building state-of-the-art machine learning solutions. Given your hands-on experience, you are surely going to find this skill easy to master.

Data science is no longer a field where knowledge of statistics, programming and basic machine learning algorithms will be sufficient. It has become a multi-faceted field where you need to master topics like recommendation engines, natural language processing, computer vision, deep learning, other state-of-the-art algorithms.

A hands-on grip in building machine learning algorithms will help you in guiding and navigating your team members through the tumultuous path of machine learning. Your hands-on background help you in mastering this skill.

Exposure to Cloud Computing

This is increasingly becoming a must-have skill for any data science professional, from freshers to leaders. Technology is advancing at an unprecedented pace and cloud computing is one of the primary reasons for it.

Cloud computing makes expanding computing power and deploying data solutions much easier – you can see why this would a critical skill to have as a data science leader! Again, you don’t need to know how to deploy a server or how a platform like Amazon Web Services (AWS) works under the hood. However, you would need to have knowledge about which platform to go for, why it’s right fit for your machine learning solution, and why your team should learn all about it.

All these questions would be answered by your exposure to cloud computing. If this is something you haven’t learned about, there are plenty of resources out there that will quickly get you up to speed. We recommend checking out the two most popular ones – Amazon Web Services (AWS) and Google Cloud Platform (GCP).

Data Engineering

Data engineering is a key part of any data science project. You’ll leverage your team’s data engineering capabilities when you’re building a machine learning project from scratch. They’ll be setting up the entire machine learning pipeline for you.

So as a data science leader, you should have a working knowledge of (at least) the below four aspects:

• Building Data Pipelines
• Moving your machine learning to the production environment
• Building Customer 360 degree view
• Understanding about Databases, Data warehouse and Data Lakes

Communication and Presentation Skill

Data Science projects are more of a treasure hunting job, the treasure being the insights you fetch from the data. The question is what is the price of the treasure? Well, that is decided by your stakeholders. The only way to get a good price is to be able to communicate how insightful the results and how can this treasure help them in improving the profits and organization.

This is where your storytelling and communication skills will come in.

Your years of experience will be quite handy here. Your audience, who will quite often be non-technical people, will look to you for solutions. You would need to weave your storytelling skills with your data science knowledge to break down complex data science terms into easy-to-understand results.

There are broadly two methods for communicating your team’s work:

• Dashboarding: A lot of data science transitioners ignore the dashboarding aspect because they focus on model building. But being able to communicate your thoughts and your key results to the stakeholder – that’s what separates a good data science professional from an amateur one. Spending time on understanding what dashboarding is and how it works will give you a huge advantage.
• Presentations: This might seem obvious but remember that you’ll be spending a lot of your time on making and delivering presentations. Again, try to work on this aspect to ensure you are coming across as a well-respected data science leader.

Staying relevant with the current update in AI/ML domain

Data science is transforming at lightning speeds that’s how the field has emerged to have high growth over the decade. What may seem relevant today may become obsolete tomorrow. So what differentiates a great data science project with a good one?

Being a data science leader, you will be able to influence the complete lifecycle of the data science project. It is imperative that you keep yourself up-to-date with all the researches happening in the field along with their applications. This will not just help you set apart yourself but also save on the cost of the project as well as the time.

We will be discussing in detail how you can keep up-to-date in this fast-moving industry.

Apply your newly learned skills to your current job role

Whatever we have covered so far has a lot to do with understanding different data science concepts. We’ve covered both the technical side (programming, machine learning, statistics, etc.) and the soft skills aspect (structured thinking and storytelling).

So, what’s the next step for you in your transition journey?

It’s time to apply your knowledge in a practical scenario! Yes, you need to marry your theoretical knowledge with hands-on practical experience to truly stand out as a data science transitioner. Given your background and your experience level, the best (and easiest) way to do this is to apply your learnings in your current data-based role.

There are broadly two ways you can do this.

Look for Data Science or Data Engineering projects within your organization

What’s the best way to apply your newly learned skill than to apply them within your own organizational structure. You could begin by entering into high-level leadership discussions and making your contributions to the project. This will help you get the platform and recognition among the high-level leadership.

Data engineering is as important as data science or any other data-based field. You must be well accustomed to this emerging field. Getting hands on a project that requires both data engineering and data science is priceless. This will set you up for a more holistic experience in the field of data science.

Work with Startups and solve business problems using AI/ML-based solutions

The innovation in the field of data science isn’t being born in research labs or conglomerates anymore, it is being driven by the spirits of startups. Looking at the recent trends, startups are popping all around the world to solve real-world problems using AI and ML.

A great experience and exposure is the one where you have complete access and control of the project and its team. This will help you in exploring the complete end-to-end lifecycle of the project. Most importantly, you won’t be limited by bureaucracy.

Open-source has led the way of innovation in the field of AI/ML. You won’t be limited by any kind of resource for your project. Though you may need to tackle other issues such as the storage costs but that’s exactly the point. Being a data science leader!

Stay up to date with current developments in the domain

This is another essential aspect of working in data science. We’ve seen the majority of transitioners skip this step and focus exclusively on picking up machine learning concepts – don’t do that!

Data science is still a very nascent field. We see major breakthroughs happening on a regular basis (sometimes a weekly basis!) and it can become difficult to keep up with all that’s happening. But if you can find time to catch up on the latest developments, you’ll already have an edge on your competition.

Let us give you an example. The Natural Language Processing (NLP) field has come a long way in the last 3 years (since 2017). We see a new language model seemingly every week that builds on the last major breakthrough. If you can keep up with this pace, if you can spend a bit of time understanding what’s going on, you’ll gain invaluable knowledge that your peers won’t have.

So what are the different ways in which you can stay up to date in the vast space of data science? Here are three suggestions based on our experience:

1. Follow Newsletters and blogs: This is the easiest way to stay abreast of developments. There are plenty of good newsletters out there (just do a quick Google search) that will send you weekly updates. You can also subscribe to blogs like Analytics Vidhya to check out the latest tools and techniques in data science.
2. Follow People: Another no-brainer! The data science community is a great place to connect with fellow transitioners, experts, and industry veterans. You’ll be surprised how approachable these experts are and they’re always willing to share their knowledge and advice. Find these people on platforms like LinkedIn and keep following them regularly.
3. Attend MeetUps: This one requires a bit of effort but the eventual payout can be HUGE. Meetups offer you an unparalleled opportunity to meet your fellow transitioners and connect with them, learn from them, and build a rapport that might benefit both parties. Over time, once you are comfortable with core machine learning concepts, you can even try and speak at these meetups to build your profile

The big salary question – what can you expect from this transition?

Making a career switch to data science for getting a salary bump is entirely justified. However, it isn’t as straightforward as you might think. There are certain things, such as work experience and your current domain, that will play a MASSIVE role in deciding your salary post-transition.

The higher you climb in the professional ladder, the more compensation you’ll expect. But coming from a non-data science field will definitely have an impact on your expectations.

We highly recommend looking for a data science leadership position in your current organization itself. This gives you a great opportunity to learn the ins and outs of the function without having to look for external roles. You can still stay under the organization’s compensation structure.

If this is not possible, then try to at least stay in the same domain. This gives you the advantage of knowing WHERE to apply data science. Because of the varying levels of experience people will bring to a data science leadership role, it’s difficult to establish a benchmark for the salary range. But it is still a well compensated field for the most part. 🙂

What are the challenges to get the “Sexiest Job of the 21st Century”?

There has never been a better time to become a data scientist. Data Science is a booming industry but it also comes with its own set of challenges. Keeping in mind that you come from a hands-on data science background, it should help you overcome the majority of challenges, however, we’ll list a few that need your special attention. If you have reached here, we know you can work out through obstacles. Let’s take them up one by one –

• Transition may take time – Data Science leadership position is a very crucial job role. Data Science and data engineering are still at their nascent stage and there are not many examples or case studies to follow. Therefore delving into data science from a hands-on role may take you little time. Nonetheless, the wait is worth its results!
• Hard to find the job –  At the leadership level, it becomes crucial not just to find the company offering great compensation but which has a supportive environment where data science is embraced and data-based decisions are at the core. To get all the requirements in one organization, you will need to work hard in job-hunting.
• Finding projects within your organization – As discussed above, a supportive organization will help you grow and magnify your perspective along with providing a new experience. Finding a data science project within your own organization will help you gain much-needed learning without job-hunting. 
• Knowledge of Data Engineering projects is also important – Data science is relatively a new and emerging field but data engineering is still in its nascent stage and organizations are in constant effort in formalizing this job role. Both are equally important. Therefore you might find it a little hard to find the right resources to study data engineering. However, you can get past this by studying data engineering from analyticsvidhya.com/blog.
• Structured Thinking – Ah, the most crucial skill yet the most overlooked one. Structured Thinking as discussed above is the art of breaking down the large unstructured problems into smaller and manageable problems. A data science project is valid as long as the problem statement is correct, otherwise, the whole project goes down the drain. Being a data science professional, you must ensure that you are working on the right problem statement.

Final Thoughts

Now that you are aware of the various components you’ll need to put together to make this career transition, are you prepared to buckle up and take this thrilling journey? The payoff is immense but as you might have gathered, you’ll face plenty of obstacles along the way. Your eventual success will come down to how well you can get past these hurdles.