Data science career goals: examples & frameworks

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Are you looking for examples of data science career goals you can set to track your career progression? Or maybe you are more interested in a general framework for setting data science career goals. Well either way, we’ve got you covered! In this article we tell you everything you need to know about setting data science career goals.

We start out with a discussion of what makes a good data science career goal and the most important characteristics you should look for when setting a science career goal. After that, we provide a framework for choosing the best career goals to help you excel in your career. Finally, we provide some examples of areas you might want to focus on when setting new career goals.

What makes a good data science career goal?

Before we discuss how to set data science career goals, we will first take a step back and discuss what a good data science career goal looks like. So what are some characteristics of a good data science career goal?

Attainable. The most important characteristic of a good data science career goal is that it should be attainable. It should not rely on factors that are outside of your control or outside of the control of your manager. Instead, a data science career goal should be an obtainable objective that you can reasonably be expected to achieve provided that you put in the effort. This characteristic is particularly important for goals that you plan to share with your manager or other members of your team. This will ensure that you develop a reputation for consistently meeting or exceeding your goals.
Measurable. Another important characteristic of a data science career goal is that it should be measurable. There should not be any subjectivity surrounding whether you have completed your goal or not. For example, instead of saying that you want to practice explaining technical projects to nontechnical coworkers, you should set a goal to schedule 5 coffee chats to explain technical projects to non-technical coworkers. This will ensure that you do not end up in a situation where you believe you have met your goal but your manager does not.
Specific. In addition to being measurable, your data science career goals should also be specific. This means that you should set a specific time frame within which you intend to achieve your goal. This helps to ensure that everyone is on the same page surrounding the terms of your career goals.
Visible. This final characteristic is not essential, but it is a big plus. You should try to select career goals that have results that are visible to your coworkers whenever possible. This helps to improve your reputation and shows that you are eager to learn and grow.

How to define data science career goals

Are you looking for a framework that can help you select the best goals for your situation? Here is a list of concrete steps that you can take to select the data science career goals that are best for you.

List the skills that are important for your position. The first thing that you should do when you are seeking to set new data science career goals is make a list of skills that are important for your position. You can also list skills that are required for the next role that you hope to progress into.
Assess your strengths and weaknesses in those skills. After you make a list of the skills that are required for your position, the next step is to assess what your strengths and weaknesses are in these areas. This will help you identify areas where you need to grow.
Seek feedback on your assessments (optional). If you are comfortable seeking feedback from a manager or trusted coworker, we recommend that you also seek feedback from your works on where your strengths and weaknesses lie. This can help you assess whether you are correct about the areas that you need to grow in.
Select one or two areas you need to improve on. After you identify the areas that you need to improve in, you should select one or two areas that you want to prioritize for the upcoming time period. Start out by weighing the pros and cons of focusing on different areas before you define more measurable steps and targets.
Set a specific, measurable target. Finally, once you have decided on the areas you want to focus on, it is time to set your exact goals. You should set at 1 – 2 specific, measurable targets per area that you decided you want to focus on.

Focus areas for data science career goals

Career goals related to technical skills

Career goals related to programming skills

Learn a new programming language. If there is a common programming language that you wish you were more familiar with? This is a great opportunity to set a career goal! Make sure that you set a concrete, measurable goal, such as implementing a specific project in the new programming language. Some examples of languages you might want to focus on are Python, R, SQL, and Julia.
Learn a new technology. If you are comfortable with a variety of programming languages but are not familiar with some of the common tools and technologies that are used to productionalize models then this is another great opportunity to set a career goal. You should set a goal to learn a new technology or framework and implement it within an existing project to demonstrate your knowledge. Some examples of technologies and frameworks you might want to focus on are Spark, Git, DBT, Docker, Flask, MLflow, AWS, and CI/CD.
Deepen your knowledge of your preferred programming language. If you would rather focus on depth than breadth, then you can focus on learning some more advanced programming concepts and implementing them using your preferred programming language. Some examples of concepts you might want to learn more about are testing, object oriented programming, and packaging.

Career goals related to statistics & machine learning

Learn about a new area of statistics & machine learning. If there is a new area of machine learning and statistics that you want to dive into, this is yet another great opportunity to set a career goal. If you prefer to learn about new subjects in a new structured setting then you can plan to complete an online course or earn a certificate. If you prefer a less structured approach, you can plan to read a book on the subject. Some examples of areas you might want to look into are time series, imaging processing, natural language processing, spatial data, network data, and advanced experimentation.
Utilize a new area of statistics & machine learning. Did you set a previous goal to earn a certificate or complete a course in a new area of machine learning and statistics? Putting that knowledge to the test is a great next step! Set a goal to implement a model or complete an analysis using methodology from that area.
Improve an existing model. Is there an existing model that you are going to be working on in the near future? You can also set a specific goal to improve performance metrics for that model by X%.

Career goals related to soft skills

Career goals related to presentation & communication

Present your results to stakeholders. If you do not regularly present the results of your work to business stakeholders who contribute to your projects then this is a great place to start. Make a goal to present the results of your work during at least X informal meetings and gatherings.
Present your results to a broader audience. If you regularly present your work to small audiences in informal settings, then it might be time to present in front of larger audiences. Volunteer to present your work in larger meetings with more formal settings such as department-wide meetings and internal conferences.
Present a technical workshop. What if you do not have the opportunity to present your work at department-wide conferences, but you still want to practice talking about technical concepts to a broad audience? You might be able to present at a workshop or skill sharing session.
Present at a conference. If you have already presented your work at department-wide meetings and you are ready for a larger audience then it might be time to look for opportunities to present your work at an external conference. Many companies will provide funding to cover travel expenses and conference fees if you are presenting your work at a conference.
Expand your network within your company. Informal communication is just as important as formal presentation. If you want to gain more experience explaining your projects to people who are not familiar with them, you can set a goal to schedule coffee chats with a certain number of people at your company to introduce yourself and discuss your projects. For maximum impact, aim to schedule these chats with coworkers you hope to be able to collaborate with in the future.
Expand your network outside your company. You can also aim to expand your network outside of your company by attending networking events in your field. This can be particularly impactful if your company is hiring, as you will likely end up chatting with some peers who are looking for new opportunities.

Career goals related to project management

Take charge of creating tickets for your projects. Does your team use tickets to track the progress on projects? Getting involved in creating tickets for your projects is a great way to start developing some project management skills. This will start to get you thinking about how much time tasks should take to complete, what blockers might get in the way of your work, and how different tasks should be prioritized.
Get involved with project planning and scoping. If you already are in charge of creating tickets for your projects, then the next step might be to get involved in scoping projects and creating project plans. Project planning and scoping is often a collaborative process that requires input from business stakeholders as well as other technical contributors. This is an important part of the data science process that helps to align your expectations with those of your stakeholders and ensure that you are delivering something of value.
Get involved with developing & prioritizing ideas. If you are already involved in developing project plans and timelines, the next step might be to get involved in developing and prioritizing new ideas for upcoming projects. Again, this should be a collaborative process that should take place alongside your business stakeholders.

Career goals related to leadership

Lead a study group. Are you looking for a way to demonstrate more leadership in your organization? Consider starting up a study group or a reading group where you and your coworkers can set aside some time to learn about a specific topic. For example, if your coworkers have strong analytical skills but weak software engineering backgrounds, it might make sense to lead a study group surrounding software engineering practices.
Manage an intern. If you are on a team that is open to hiring interns, you may be able to manage an intern or co-op student. This goal will require a conversation with your manager and team leadership as well as some planning, so you should start these discussions ahead of time.
Mentor coworkers on your team or other. If you have other members on your team that are new to the company or are more junior than you, you may also be able to formally mentor those teammates. You may also be able to mentor coworkers that have similar levels of experience to you if you have expertise in a certain area that they are trying to grow in.
Lead a multi-person project. If you are currently working on individual projects or projects that are led by other people on your team, you can also demonstrate leadership skills by taking the lead on an upcoming project that involves multiple people.
Lead a revamp of a process. If you work on a small team that does not hire interns or work on multi-person projects, there are still options. Perhaps you can take the lead on revamping a process that your team uses or introducing a new tool.
Ownership of outcomes. Finally, you can take formal ownership of outcomes in a certain area that you work on. Again, this will require a conversation with your manager.

Data science career progression and scope of ownership