AI Guild #datacareer

The Validated CV

Cutting search time and cost for a 1st and 2nd data role in the industry and startups

Finding a 1st or 2nd role can be straightforward, or it may be a bit of a struggle. Getting more interviews: It helps if you know what you are looking for and how to convey what you have to offer convincingly.

Based on prior work with 100+ practitioners on their CV and professional mission, the AI Guild guides developing your professional mission and provides support in writing a validated #datacareer CV for impact with hiring managers and companies.

This post supports you in submitting a draft CV to the AI Guild for vetting. We use your CV to offer you the following.

1. A conversation to help you make the best choice concerning the next data role, type of company, and industry or domain.
2. Improve your CV to maximize impact with readers such as hiring managers and human resources.
3. After vetting, an AI Guild imprimatur that recommends your CV.

Your draft CV will be the starting point for the personal conversation and the foundation for vetting. It makes the process more effective and maximizes your chance of finding out what kind of data career is your best choice.

Technical Skills

Sometimes we see a CV with a long list of technical keywords or a CV with a mixed list of technical and business skills. This approach makes it difficult for the hiring manager to understand or evaluate your profile.

Here is a suggestion on how to structure your technical skills section:

1. Organize your technical skills by category or concept; and
2. Indicate the proficiency level for programming languages and perhaps some of the essential tools you use.

Categories like programming, databases, visualization, statistics, mathematics, analytics, and modeling help guide your reader and enable human resources to understand your profile. Use categories to organize and highlight your skills profile.

Alternatively (or even additionally), you can use concepts like data analytics, science, and engineering, or machine and deep learning for profiling your technical skills. Such concepts highlight how you match the typical roles in a company.

Especially for Python, R, and SQL, it makes a difference if you have been a regular user for 1 or 3 years. Please indicate your level of proficiency, e.g., state the number of years or use a visual system (like dots) to enable your reader to grasp your skills level at a glance.

Moreover, depending on your profile, your level of proficiency also matters. For example, if you are interested in machine learning, you should demonstrate (some) proficiency in scikit-learn.

In sum, please remember that the technical skills section very often is the first impression that your reader has. Please make it easy to understand your profile.

Data Experience

Experience matters. We noticed that people starting a data career often have experience with data — from, e.g., online learning, university, internships, side projects, and competitions — but make little of it on their CV.

Here are two suggestions on how to make your experience count more

1. The vignette, i.e., describing a relevant data experience in detail.
2. The data project list, especially if you have accumulated relevant experience over time.

The vignette means you can describe a specific project or experience in simple terms — not bullet points but prose with a compelling title. It works well if you do one of the following in 60–75 words (4–5 lines).

• Communicate the data project process and impact to a layperson; or
• Answer the what, when, who, where, why, and how of your project; or
• Go into detail on the type and size of the data and its handling, and show the usefulness of your work.

The project list means that you have a portfolio already, possibly also from a first role or previous job. The idea is to list your best 3–5 projects, write a vignette, and provide a repository link.

Why is data experience significant? For companies, it is the foundation of expertise. Some consider data engineering the foundation of all roles, e.g., data analyst, data scientist, machine learning engineer, a software engineer. Even if a company has dedicated data engineers, your experience with data, and especially with data in your chosen domain or industry, remains the foundation of your work.

Organization and business skills

Some CVs we see include a list of soft skills. Also popular is a statement that one is, e.g., a highly motivated professional, team player, self-starter. While these claims indicate how you see yourself, they do not reveal what you have done and what your proven skillset is.

Here are two suggestions on presenting your business skills convincingly. They help you not only get more interviews but also help structure some of that interview conversation.

1. If you already have business or industry experience, review your track record, highlight 2–3 proven skills by giving examples, and add 1–2 developing skills.
2. If you are transferring to industry (after a Ph.D. or Master), then use the notion of transferable skills to highlight 3–5 skills you have acquired that are highly relevant in a business environment, and provide examples.

Business skills are not just soft skills. Please consider the full set of skills you have, such as analytical skills, sales, funding, business development, communication, presentation, reporting skills, or leadership experience.

For each skill that you claim, you would want to substantiate the skill with a couple of examples that clarify how much or how often you exercise the skill and to what effect, e.g.

• Extracting information from large datasets, i.e., more than twenty thousand features from hundred-plus samples.
• I have given 15 international conference presentations in 3 years with audiences of 150+ in languages X and Y.
• I managed two international teams with up to 10 members by coordinating the project plan and supervising the delivery of the project work packages.

The section highlighting your business skills is an excellent way to close your CV. It reminds the reader of the (potential) business value you bring and indirectly serves as a call-to-action to send you an interview invitation.

Professional mission

Almost all CVs include a profile statement or summary. It is often the second thing we look at — after glancing at the technical skills section. Yet, all too frequently, this statement at the top of your CV focuses too much on the past, emphasizing previous positions or roles.

Here are two suggestions on maximizing the value of your profile statement and make the reader take note.

1. Use a clear and straightforward professional mission statement. Some of the best and most exciting mission statements are one sentence only. They clarify how you change the world and make it a better place. Here is an example from an AI Guild member: “I am bringing machine learning skills to the energy industry to help decarbonize the supply of heat and electricity.”
2. Provide a forward-looking profile and search statement on your next challenge.

For a forward-looking statement, you would typically open with the role you are looking for, e.g., I am a Data Scientist or a Machine Learning engineer. If you had a different role previously, you would next mention that as your background. The second sentence would highlight your expertise and experience. A third sentence would clarify what domain(s) and type of companies interesting to you. A final sentence indicates where you are looking (locations) and from when you are available.

The forward-looking profile statement’s value is that the reader can quickly determine if there is a (potential) match. Also, such a search statement helps very much with referrals. If it is obvious what you are looking for and when your network can also act on your behalf, make connections.

Submitting for the AI Guild CV check

For the AI Guild CV check to be useful, we ask you to submit a complete draft CV with all the elements described above. Typically, we would expect the CV to be two pages. On occasion, your draft may also be 1 or 3 pages.

You may submit directly here.

Originally published at https://www.datacareer.eu on March 12, 2021.