Build Your #Datacareer Competency Profile

How to accelerate careers towards the senior practitioner

The #datacareer Coaching Program: Towards the Accredited Senior Professional

The AI Guild is the go-to community for data scientists, data engineers, data analysts, and machine learners in startups and industry — as well as business professionals advancing AI adoption.

The AI Guild is engaged in #datacareer coaching for its members and supports career development. For senior practitioners, we focus on leading and building teams. For the early years, competency development, job search, and specialization are the key success factors.

Based on AI Guild experiences and insights, this post sets forth the notion of a #datacareer competency profile. The competency profile is the set of knowledge, abilities, and skills needed for a particular data-role so that your professional life is both meaningful and successful. Our starting point is working on the competency profile that you need for a senior position in

  • Roles such as data analyst, data engineer, and data scientist;
  • Specialist fields such as NLP, Computer Vision; and also
  • Product and business roles related to data and artificial intelligence.

For you, the practitioner in the first 5 professional years, we aim to achieve the following:

  1. Be able to assess your competency profile through your CV, résume, and portfolio, as well as your continuing professional development;
  2. Provide guidance on what to prioritize in your professional development to achieve senior status as soon as possible;
  3. Offer accreditation by the AI Guild that you have achieved the senior competency level.

You can check out the pro bono workshops we already provide for the practitioner community worldwide, e.g., on roles such as data analyst, data engineer, and data scientist in industry and startups, on job interviews and coding challenges, and on promising use cases in the data economy.

Look at example competency profiles

Here are example competency profiles that accelerate careers and represnet must-have profiles for companies deploying. Please click on the link for Data Analyst, Data Scientist, Data Engineer, ML engineer, DL engineer, and Computer Vision engineer.

Why build #datacareer competency profiles?

As the data economy evolves, specialization leads to the emergence of new data roles and specialist fields are progressing at a fast pace. Companies and managers often have little capacity or experience to support career development. #datacareer coaching empowers practitioners to build their careers more intentionally.

We have observed some issues, which we see corroborated by the reports and experience of leading industry practitioners worldwide, most importantly:

  • Although data role specialization is increasing, data career paths seem not so well understood by companies and practitioners alike;
  • While promotions are happening and companies are keen on hiring senior practitioners, it seems complicated to gauge the required quality standards for senior roles effectively; and
  • As team play increases for practitioners across roles in business, product, and data tech, it becomes essential to understand how, on the one hand, professionals in data roles better achieve business impact and, on the other hand, how much technical know-how and skills the business and product people need.

Competency and competency profiles is an academic subject as well as interesting to high-performing organizations (…and the search engine of your choice will tell you more). Yet, our intention is both more pragmatic and more fundamental. Pragmatically, data careers are only an emerging field, and we want to support practitioners and companies in advancing adoption. We believe that this requires a fundamental intervention in two ways:

  1. The elucidation of the core competencies for a data career;
  2. The building of competency profiles for a variety of data roles.

This is no small effort. Internationally, work has only begun on competency profiles and career ladders (see references below). Also, because the profession is new, and the vast majority of practitioners are in the early stages of their career, we choose to focus on defining the ‘senior’ competence level, and the step from junior to senior practitioner first.

There is historical precedent in recognizing a senior competence level in professions as diverse as medicine, law, and engineering. An interesting example is the chartered professional (e.g., chartered accountant, chartered engineer, chartered statistician), a concept emerging in the mid 19th century in the UK. Accreditation as a senior professional is a rigorous process based on standards, quality assurance, and continuous professional development.

Join the AI Guild #datacareer coaching program

Setting the standard

Involving the AI Guild community of senior and expert practitioners, we develop competency profiles across all data roles in analytics and machine learning. The AI Guild sets the standard and provides quality assurance for practitioners and companies alike by accrediting senior practitioners.

In what follows you will find an outline of the following

  • Competency Profiles: Our working hypotheses on the core competencies and starting points for benchmarking senior competence levels.
  • For AI Guild members: A first description of the #datacareer coaching for which you can apply to 31 August 2020.
  • For companies: How your company could collaborate with the AI Guild and what we would expect.

What might be the core competencies of a data career?

If you are enjoying a data career, you likely have a university education, a numerate background, and invested time in learning data skills. Working in an organization or company, you know there are behavioral expectations (e.g., team player for the workflow), functional requirements (e.g., version control, timesheets), and ways of handling business and politics in an organization. So what might be the core competencies you need to improve on for a great data career?

Typically, we look at technical skills across data systems, machine learning, and software engineering combined with personal and organizational skills. This perspective has its merits in established labor markets and often works well for organizations, recruiters, and candidates.

Yet, the field is new. Its practitioners often are pioneers. It is the early days for data careers. Therefore, we postulate that practitioners seeking to advance AI adoption best develop a competency profile befitting the historical situation. After all, the practitioners in data and artificial intelligence are leading on the next industrial revolution. What kind of core competencies do we need to see and develop?

Firstly, data career practitioners need a sense of mission. If you understand why you are doing what you are doing, you will have purpose and meaning in your professional life. This facilitates one’s commitment to the cause of advancing AI adoption, and the commitment will help you shape the field while also seeing you through tougher times.

Secondly, you have your data skills. Key trends include the increasing data role specialization and the rising value of domain expertise. The relevant domains are the industry vertical as well as the particular focus, e.g., time-series, images, language. Moreover, as data models increasingly move to production, additional roles are emerging around maintenance and continuous integration and delivery. If your primary role is in business or product, then your core competency will be in this role. The technical skills will be the other variable, depending on how you are interfacing with technical roles.

Thirdly, as you are shaping your work’s impact, you will be developing a set of business competencies. This set of competencies is variable, depends on your role, but is also indispensable for advancing AI adoption. For example, if your role is product-oriented, you will be acquiring skills to interface with users and customers.

In sum, here is a suggestion of how to think about #datacareer competencies. It isn’t about job descriptions, and also not about promotions, at least not primarily. Instead, it is about the core competencies you need in an ecosystem characterized by the scarcity of expertise and employers needing to scale expertise in the next decade. After all, companies and practitioners are still learning how best to deploy use cases for data analytics and machine learning. The question to you is: Are you willing to build the competency profile for leading on the use cases in your domain and company?

Source: Sequoia Capital Publication, May 2019

Building competency profiles for senior roles

For the purpose of advancing AI adoption, the AI Guild is committed to building competency profiles for data careers across roles in tech, product, and business — both for functional tracks and management tracks. Thanks to the pioneering work of Michelangelo D’Agostino (astrophysicist turned data scientist) and Katie Malone (particle physicist turned data scientist), we have a starting point.

Here is an example of the technical and soft skills career ladder from their publication. For Data Scientists, it outlines the expectations for junior and senior-level and the difference between them.

The technical skills expected, according to Katie Malone and Michelangelo D’Agostino.

Source: D’Agostino and Malone, 2019, page 39

The soft skills expected.

Source: D’Agostino and Malone, 2019, page 42

We seek to build on this important work. Firstly, we broaden the focus to the professional mission, data skills, and business impact from the start, enabling professionals in their early years to understand clearly the best way forward to senior level and leadership roles for them. Second, the work of the AI Guild will center on developing the notion of a career ladder not just for data scientists but also other data roles such as data analyst and data engineer, as well as product and business roles. Thirdly, and in parallel to launching the coaching program, we are gearing up to define senior competence levels and offer accreditation to practitioners.

For AI Guild members: Building your competency profile

What is in it for AI Guild members? We invite members to apply for the #datacareer coaching program during the early years of their professional careers. You are eligible to apply after having completed the first twelve months in the field.

The key points of the #datacareer program

  • Our initial capacity is 20 new coachees per quarter, and we set up each cohort as a peer group with structured interaction.
  • After your successful application, you get an initial review of your competency profile and guidance for the first year.
  • You may request two more reviews of your competency profile — after one and after two years.
  • In between annual reviews, we run dedicated online sessions for the active cohorts.
  • The maximum duration of your program is 3 years.
  • At the end of the program, you have the right to submit your CV and portfolio to the #datacareer board for recognition by the AI Guild as a senior practitioner.

For companies: Setting standards together

If your company is building great data teams, you can join the program too. There are two ways in which you can do this:

  1. Work with the AI Guild experts on creating a bespoke program for scaling expertise at your company.
  2. Join the AI Guild #datacareer program as a supporter and sponsor, and have your data talents apply on equal terms to the program too.

If you are interested, please get in touch.


DJ Patil “Building Data Science Teams”, free ebook

Camille Fournier “The Manager’s Path: A Guide for Tech Leaders Navigating Growth and Change”

Michelangelo D’Agostino & Katie Malone “The Care and Feeding of Data Scientists. How to build, manage, and retain a data science team”.

Sequoia Capital Publication “Progression Of A Data Scientist”, May 2019

Thank you

Thank you to the 100+ AI Guild members that have participated in the career coaching and have thereby shaped the AI Guild perspective. A special thank you also to the organizers of the career coaching, i.e., Filipe Conceição, Fahrnaz Jayrannejad, Chris Armbruster, and Sahar Hashai. Further thanks to the reviewers of this text, i.e., Marija Vlajic Wheeler, Irina Vidal Migallón, and Dânia Meira.

The AI Guild stands for diversity, an ethical approach, and a sense of mission of advancing AI adoption for the benefit of sustaining humanity on this planet. You will find this reflected in our membership, as well as in who is organizing our activities and mentoring on them. You can check this out via our founding letter and the community members.



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