When you are the expert and leader
"The AI Guild accreditation is similar to the architects' association. The aim is to prove that one has enough industry experience to be called an expert data scientist."
I am the Director of Data Science at Bayes Esports, the premier destination for data in esports. As Director, I am responsible for data quality and models used in games, competitions, and betting. I also accompany the professional and personal development of the team members.
The professional field of "Data Scientist" is still very new, and there are no established quality standards. Anyone can take an online course on data processing and then call themselves a "Data Scientist.” Unfortunately, this is somewhat like me changing tires at a self-help garage and becoming a car mechanic. While it's essentially clear what to expect from a senior software developer, it's pretty fuzzy for a senior data scientist.
The AI Guild is setting the stage for increasing professionalization in the career field by having an ongoing discussion among members about what a career as a Data Scientist might look like, what skills are needed at what level of development, etc. The many roles that are part of the Data Science field, such as Data Engineer, Data Analyst, MLOps Engineer, etc., are defined within the AI Guild.
In addition, this professional organization provides a forum for Data Scientists to talk about ethical issues, plan their careers, or see what challenges colleagues in other companies are facing. It is vital for a field where lateral entry has been typical.
Proving experience and knowledge
Accreditation of experts is an essential part of this concept. Often, you only find a senior data scientist in a company - the top management cannot judge exactly how much this person can, or should, do. Like at an Architects’ Association, the AI Guild experts go through a peer review and prove their experience and knowledge.
The essential requirement is, first of all, professional experience of at least four years. In addition, there are four competency areas in each of which the candidate must demonstrate a high standard:
Data Proficiency. Candidates must demonstrate significant experience with data analytics and machine learning by carrying out projects from start to finish.
Advancing AI adoption. Has the candidate created solutions that have brought demonstrable benefits to a company? Can the positive impact of using AI be demonstrated?
Professional Contribution. Is the person able to communicate clearly about their work? Are they continuously educating themselves? How have they shared knowledge and insight?
Personal Capacity. Is the candidate a role model for ethics and responsibility? Are they someone newcomers can look up to?
Be a role model
I chose to be accredited by the AI Guild because, as a woman in a senior technical position, I automatically have a role model function that I want to fulfill. Through the accreditation from the AI Guild, I make myself visible as an expert. In addition, I also expect a personal advantage in my career - of course, my resume speaks for itself. Still, I also hope that the accreditation will sooner or later establish itself as a professional distinction.
Dr. Darina Goldin drives forward the use of machine learning in the esports industry. Darina is recognized by her peers as performing to the highest quality standard. She has successfully developed AI-based solutions that match customer needs and industry requirements to create lasting value.