I love meeting with prospective and aspiring data scientists. Equally, I enjoy meeting with current data science professionals who are looking to level-up in the field. Selfishly, these kinds of meetings help me learn about and better understand the challenges folks face as they look to enter or level up in the field.

The Best Use of Time

Meeting with potential clients is one of the best ways I can spend my time as a data science career coach.

The other, less selfish reason it is important to meet for a discussion is that often folks have specific questions in mind and are looking for my insights. For example, folks looking to transition into data science might be asking:

• What salary can I expect after I transition into data science?
• What will the work be like after I make the transition?
• What skills do I need to know before the transition? What skills can I focus on learning after the transition?
• Can I teach myself data science? Do I need a degree? Will a degree help (even if it isn't necessary)?
• Are bootcamps a good option, for someone like me? If bootcamps aren't a good option for me what courses (if any) should I look at?
• What is the best job search strategy? What is the most effective job search strategy?
• How can I connect with recruiters? How can I connect with hiring managers? Should I connect with recruiters or hiring managers?
• And more.

Most of these questions are difficult to answer in a broad and generally applicable way. For most folks, the answers to these questions will be unique and different.

Personalization: A Core Value

A core business value of mine is to avoid over-generalizing and to focus on providing individualized and thoughtful advice specific to the person I'm focused on in that given conversation.

The purpose of this article is to share a handful of the questions I often ask when getting to know folks new to the field - and why they're helpful in really getting to know a person.

I would like to ask you more about your work in the most recent portion of your career?

Why it's important: Your recent work experience dictates your current skill set, domain knowledge, and the relevance of these skills to data science. The closer your recent experience is to data science tasks, the more transferable skills you might have, potentially leading to a higher starting salary. This tells a lot about where your passions currently lie and what kind of challenges or tasks have recently motivated or demotivated you.

I would like to hear more about your early career experiences?

Why it's important: Even early career experiences can offer valuable insights into one's ability to adapt, learn, and tackle new challenges. These experiences can also point towards skills or knowledge areas that might be relevant in data science but aren’t obvious at first glance. Your beginnings can reveal your roots and foundational values. It can show how you've evolved over time and what formative experiences shaped your professional journey.

It would help to know about other previous major career transitions you've made?

Why it's important: Previous transitions can provide insight into your adaptability, learning curve, and how you handle change. It can also indicate how quickly you might be able to ramp up in a new role in data science. Transitions can unveil resilience, risk-taking, and adaptability. They can also provide insight into what drives you to seek change or growth.

What training in stats, data science, and programming that you've already done?

Why it's important: Direct training in these areas is crucial for a data scientist. The more advanced or comprehensive your training, the better positioned you might be to command a higher salary. Becoming a data scientist is open to those willing to learn these skills, if you do not already have them. Your stage in this journey also reflects your commitment to self-improvement and perhaps natural curiosity or inclination towards analytical and problem-solving tasks.

What training in stats, data science, and programming that you're able to do moving forward?

Why it's important: Your willingness and ability to further your education indicates your commitment to the transition. Organizations may sometimes view candidates who engage in continuous learning more favorably.  Your willingness to learn speaks volumes about your forward-looking attitude and an intrinsic drive to better yourself and adapt to the ever-evolving tech landscape.

I, of course, also need to know what your preferred timeline is?

Why it's important: Transitioning quickly might mean accepting a position at a lower starting salary (at least at first). Conversely, taking more time might - might - allow for more training and networking, potentially leading to a better position and salary. Your timeline can reveal more about what brings you to look at the transition. A pre-determined sense of timeline can also help show your approach to planning. This topic can also hint at external factors in your life influencing your decisions.

It'll be important to know what kind of connections (personal or professional) you already have in data science?

Why it's important: Networking plays a significant role in job hunting. Networking is not, not, not the most important factor however. Having your own connections can lead to opportunities that might not be publicly advertised, or it could give you a competitive edge in the hiring process. I can also sometimes quickly help you identify connections you didn't fully consider as important or useful to you in your plans to transition. The relationships you’ve formed can provide insights into your networking style, interpersonal skills, and how you value professional relationships.

For many I also need to know if indeed you're aiming specifically for data science (or something adjacent)?

Why it's important: Salaries can vary widely between data science roles and adjacent roles like data analyst, data engineer, or business intelligence specialist. Knowing the specific role can provide a more accurate salary estimate. Your target role can show where you see your strengths lying, or perhaps what kind of work you find most fulfilling. Often, I am able to help folks refine their target - even during a brief conversation. I can help you make sure you have not aimed too low, for example. I can help you make sure your aspirations are not too modest.

I need to know what kind of organization you want to work for (gov, corporate, non-profit, educational)?

Why it's important: Different organizations have different salary structures. For instance, corporate roles might generally offer higher salaries than non-profits, but they might also come with different work-life balance or job expectations. This choice can hint at your values, priorities, and what kind of impact you wish to make through your work - which could have an influence not only on your short term career (or compensation) but on your long term outcomes too.

And a similar, but different question is, the kind of organization you prefer to work for?

Why it's important: While the first question focuses on practicality, this one touches upon personal preferences, which can impact job satisfaction and longevity in a role. Both aspects can influence the kind of compensation package an individual might be willing to accept. Your preference showcases what kind of workplace culture resonates with you and where you feel you'd thrive and contribute the most.

Whether you're already communicating with recruiters?

Why it's important: Engaging with recruiters can give you an insight into current market trends, available opportunities, and how you compare with other candidates in terms of expected salary. This can indicate your proactiveness in pursuing opportunities and how invested you are in making the career transition. And I can often help you find creative ways to begin conversations with recruiters if you have not already done so.

What kind of input you've had from recruiters or others (if any)?

Why it's important: Feedback from industry professionals, especially recruiters, can provide a realistic perspective on where you stand in the competitive landscape and what kind of salary you might expect given your qualifications and experience. Feedback reception can reveal your openness to criticism and your ability to adapt based on external inputs. Hearing what feedback you've had from others (if any) can also help me provide you with a more well-informed range of inputs.

Conclusion

In today's rapidly evolving data science landscape, transitioning or advancing in the field is a decision that comes with its own set of challenges and queries. From understanding the potential salary spectrum to discerning the skills necessary for the shift. Aspiring data scientists and professionals seeking growth are often inundated with uncertainties. As a data science career coach, my interactions with you not only aim to enrich my understanding of your challenges but also aim to meet your need for personalized advice over broad generalizations.

The right guidance can be instrumental. To offer good advice, I look deep into understanding your professional background. I look to understand your recent roles and your early career experiences. Assessing your adaptability through past career transitions, evaluating your present training in relevant skills, and exploring your openness to future learning are some of the most critical factors.

Furthermore, understanding your desired transition timeline, existing connections in the field, specific role aspirations, organization preferences, and your current standing with recruiters helps me tailor my advice.

In essence, the field of data science is vast and diverse, and to navigate it successfully, one needs more than just technical acumen. It requires a keen understanding of one's own professional trajectory, strengths, values, and goals. And as a coach, my aim is to bring clarity to these aspects, ensuring that the advice I offer is both relevant and actionable for every individual I converse with.