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How to build a technology narrative for early career data and analytics talent acquisition


We provide advice for companies in industries still going through a digital transformation on how they can start to understand the problem that Data and Analytics professionals can help solve.



By Jacob Gelbaum

Starting to understand the problem

Companies in industries still going through a digital transformation (HealthCare, Insurance, Banking, Physical Products, etc.) need to compete for junior Data and Analytics talent differently.  Early career candidates are generally unaware of the true breadth of industries and companies where their skills, talents, degrees, and certifications are valuable.  Additionally they haven’t yet gained a perspective on the differences of what it’s like to be an employee between companies:  large vs small, established vs start-up; or how to evaluate mission/vision/values, or understand the full impact of a benefits package on their new independent life.  This applies to undergraduate and graduate, but also to non-traditional candidates as well (not college grads / or non-technology degrees), who struggle navigating the hiring process without traditional degrees.

The problem gets more complicated

As technology focused universities, and specialized on-line training courses, continue to ramp up special degrees and certifications in data and analytics, more and more early career candidates come out of these programs with similar gaps in knowledge about employer options, pros and cons.  They know the big names in technology, and generally want to land a job building algorithms day-in, day-out.  What they usually don’t realize is that the companies they haven’t really considered, AND the big names they know, have limited numbers of jobs in core algorithm development and AI.

Frequently, the most important jobs to fill in both these types of companies aren’t AI jobs.  Trends in machine learning make this even more true as open-source ML engineering frameworks mature and companies are beginning to say that “there is no shortage of people able to train models on a dataset, they need engineers that can build data driven products.”  This gap in knowledge could be where digitally transforming companies, and non-traditional technology companies, change the game.

Career Advice

Problem Statement

If most early career candidates have their minds set on building new algorithms and models, and if only a small percentage of the growing digital workforce is doing that kind of work, how do you get early career talent excited about all the digital opportunities at your company?

The solutions space

As I talk to early hires, conduct workforce strategy analysis, and engage on college campuses, the message I think will work the best is product development.  Build teams with a variety of skills, tied to a product, that represents an end-to-end solution.  This concept resonates in our era of agile and collaborative solutioning, designing, and building.  These products teams are composed of people who have talents in many areas, and who may serve a few different roles depending on the product or sprint.

This may be a daunting message to deliver when your company is struggling to complete the agile transformation, still doesn’t have a fully service enabled data architecture, or isn’t completely migrated to the cloud.  Not competing for this talent isn’t an option, because acquiring this talent makes closing on those transformational goals a reality.  Here are some generic messages worth trying.  They can be tailored to any company struggling in this area:

  • “We build analytically driven products by putting together teams of cross functional skills that can understand the full spectrum of our customers’ needs.
  • “We have dozens of these products, and product teams, targeted at our or global customer base”
  • “We equally value all the variety of team skills that help us meet business objectives for a product…
    • …Business Analysts that work with product owners to design analytic solutions;
    • Data Engineers who prepare and integrate available data;
    • …System Architects that design a solution that scales;
    • …Analytics Engineers that build algorithms and monitor the models;
    • …and Software Engineers that effectively implement the designs”
  • “Our digital transformation journey…
    • …requires that this number of product teams will continue expand more quickly over time, providing opportunities to grow and try new things.”
    • …is expanding the library of available datasets ready for analytics based products.”
    • …makes the integration and exploration of new technology critical for the success of these product teams.”
    • …increases the exposure of these teams to technology and business leaders.”
  • “Come help us continue to build momentum by considering an established company that’s committed to this journey.”

Leaders and hiring managers in these companies might do well with early career talent to bring examples of these products to light in their conversations and local community building efforts.  They should also work closely with their local HR and Talent specialists to pair these messages with more traditional ones.  Demonstrate how ongoing Corporate Social Responsibility relationships in the community directly connect to the Mission/Vision/Values.   Explain the considerable ancillary career expanding benefits (Annual Incentive Plans, 401K matching, flexible collaborative working options, considerable continuing educational opportunities) of working at an established company.

Going up against the big names will never be easy, but integrating these messages makes you authentic about your journey, and considerably strengthens your pitch.

BioJacob Gelbaum is an IT professional, with +15-years in data and analytics. His early career included skills in M&S, geospatial analysis products, and cross functional analytic teams. He grew into roles where system development projects, and programs, turned data and analytics into enterprise character traits. More recently he delivers IT Strategy support.

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