• Chris Armbruster

Data Analyst (m/f/d). A competency profile.

The competency profile validates your domain expertise in data. It recognizes you as a Senior practitioner or advances your career to the Senior level. By practitioners, for practitioners - this service is provided by AI Guild members recognized for their expertise.

Here is the example of a Data Analyst.

She has accumulated five years in advanced analytics and modeling as a postdoc and in the automotive industry. Her profile shows her expertise at the forefront of innovation in technology and business.

The competency profile is built as part of the AI Guild career development program.

Data Analyst competency profile
A data analyst invested in ML and business innovation

What do you see in the competency profile?

The training in experimental methods is vital to her. Yet, she has shifted decisively to the industry, working at the interface of technology and business. She uses analytics to incubate a disruptive idea and provides blueprints for new products. Her technical skills are essential in two ways.

  • Data-driven exploration of new products.

  • ML-based product development.

Furthermore, her leadership interest and skills are emerging at the said interface (leading technical teams).

Product and project

Deploying ML invariably means releasing a product, even when the product is an in-house service. I have seen companies struggle with the difference between product and project. Project management has been vital to business for decades, e.g., optimizing processes, onboarding consultants, seeking innovation. But if you treat machine learning as a project, you are unlikely to progress beyond temporary data infrastructures and proofs-of-concept. For European corporates, this is an issue frequently.

What can the practitioner do? You can make the most of it by building your skills on two tracks.

  1. Hone your business innovation capacity by developing disruptive data-driven products on established markets.

  2. Build out your predictive modeling skills and seek opportunities for deployment.

Where is her #datacareer headed?

In terms of industry experience, this is a great mid-level profile bolstered by a long-term investment in critical ML skills like time series and computer vision. The profile emphasizes Data Analytics as a business-oriented role (working with technical teams). I think she is also open to a role switch. She has the skills to be a deep ML domain expert.

I sense that this practitioner is at a critical career crossing. Once she has chosen which road to travel by, I expect her to rapidly progress to the senior level.

How to get your competency profile?

Are you in Year 2, 3, or 4 of your #datacareer? Would you like to find out how you efficiently show and develop your expertise? It takes 60 days to complete the competency profile. To start, point us to your LinkedIn or GitHub profile (or similar) here.


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