• Chris Armbruster

Data Scientist (m/f/d). Competency profile No 3.

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 Scientist.

She has accumulated years of data experience as a postdoc and in the industry, using Python, R, and SQL. Valuable is that she created data infrastructures many times, can judge similarities and differences, and knows how to secure quality outcomes.

She has a background in life science and now works in eCommerce.

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

Competency profile for a Data Scientist
A Senior-level profile of a Data Scientist with leadership potential.

What do you see in the competency profile?

You are looking at the profile of someone capable of impacting a company's effort in two ways.

  1. Data Ops: Building a high-quality infrastructure for data analytics and machine learning, also; if the data sources are disparate, some are historical or first need creating.

  2. Team leadership: Her experience with the entire value chain from Data Ops to orchestrating delivery means she is qualified to secure quality deployment overall.

How did she achieve this profile? When switching from academia to industry, she chose an operational role focusing on product, pricing, and the customer (e-Commerce optimization). With this choice, she accumulated business experience rapidly.

Consider a different move, e.g., proof-of-concept modeling, analytics dashboarding, database solutions. Such a focus would not give you 'frontline' business experience. If you are moving from academia to industry after your Ph.D., focusing on the product and customer is the better move because it accelerates your career.

Startup challenges

Startups typically generate digital data very early. Yet, I am often bewildered how even established startups (Series D or later, post-IPO) don't make the most of their data.

In eCommerce, for example, I don't see many European startups progressing much beyond offering a catalog with pretty pictures. Perhaps the company is good at fraud detection for payments or has a good marketing analytics team. Yet, the opportunity is to build an integrated data infrastructure that drives growth while delivering a much-enhanced customer experience.

The value I see in the competency profile of this Data Scientist is the capability to contribute to startups building their end-to-end ML platform based on integrated data.

Where is her #datacareer headed?

Her industry experience is two years, yet this is a Senior-level profile with leadership potential. The reason is the extensive experience (8 years) in building data solutions combined with mastery of the complete value chain to deployment impacting the business's bottom line.

It is a must-have profile for companies with customer-facing ML deployment.

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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