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2#16 - Pedram Birounvand - Data Skills for the Future (Eng)

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Manage episode 364592892 series 2940030
コンテンツは Winfried Adalbert Etzel - DAMA Norway によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Winfried Adalbert Etzel - DAMA Norway またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

«When technology evolves really fast, also the skills you need to hire for evolve really fast.»

Within a rapidly changing environment, fueled by technology and great ideas, it can be hard to define a stable career path. So I brought in an expert on developing companies and building legacies. Pedram Birounvand has a background in quantum physics, data engineering, experience from Spotify and moved into private Equity 6 years ago. Now Pedram started a new chapter in his career as the CEO of a startup working with Data Monetization.

Here are my key takeaways:

Data Skills for the Future

  • As a leader in the data domain, you need to be a storyteller, to tell the story about the necessity of data, like quality or governance.
  • The Job titles for Data Scientist, Data Engineer, etc stayed consistent, but what we expect from someone with that job title changed greatly through the last years
  • Hard skills in data are not as important as they where for every company
  • Make sure you know, what you are optimizing for in your career
    • Are you optimizing for flexibility, self-employed consultant is best
    • Are you optimizing for building a legacy, be an entrepreneur
    • Are you optimizing for leading people and see people grow, become a line manager
  • Dont become a manger if your passion is within engineering. You will need to optimize your time for coaching people, not working on problem solving as an engineer.
  • «The technology of applying AI and ML becomes more and more simple and becomes more and more commoditized.»
  • Dont hire Data Scientist to build models that you can buy out of the box.
  • Don’t hire Data Scientist if you need Data Analysts. They work entirely different and the work is not comparable.
  • If you hire a Data Scientist before having good Data Engineers, then the Data Scientist cannot create any value
  • «In order to be successful as an engineer, you need to have a really transformative mindset.»
  • You need to enjoy the learning process, if not focus on something else in the IT-domain
  • Adopt an agile mindset. Agile fundamentals are key to todays work life.
  • «You need to embrace to be able to incubate things.» Build incubator squads as soon as a good idea pops up.

Recruitment

  • In a small company you need to be much more flexible and broader in the way you tackle problems, than in a larger company where you can be more specialized
  • As a hiring manager, don’t lean too much on the titles, but make sure you understand what you need in your company. This is key to writing a good add and attracting the right talent
  • In a job advertisement be specific: What does it mean to be a Data Engineer in the context of your business?
  • What is important for you as a company today, based on the trends coming?
  • Building code has become so much simpler. Do you still need developers that need to know all the details about a certain language?
  • Maybe a person that can be close to the business, and not so deep in programming can add more value?
  • «You have to know what it is you are optimizing for.» If you have an extremely complicated technology stack you need deep knowledge, if not don’t hire it.
  • In a recruitment process, focus on soft skills of rapid learners that can adjust to new situations and having an interest in understand your business use cases.
  • «My interview secret: Share a whiteboard session with me»
    • Try to figure out how self-sufficient a candidate can be in understanding how the business works and where to get relevant data
    • Test how candidates react in uncomfortable situations, with customers that are not always happy about results and solutions.
    • Look for candidates that show resilience in new and uncomfortable situations
  • Career models should be technology agnostic
  continue reading

68 つのエピソード

Artwork
iconシェア
 
Manage episode 364592892 series 2940030
コンテンツは Winfried Adalbert Etzel - DAMA Norway によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Winfried Adalbert Etzel - DAMA Norway またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

«When technology evolves really fast, also the skills you need to hire for evolve really fast.»

Within a rapidly changing environment, fueled by technology and great ideas, it can be hard to define a stable career path. So I brought in an expert on developing companies and building legacies. Pedram Birounvand has a background in quantum physics, data engineering, experience from Spotify and moved into private Equity 6 years ago. Now Pedram started a new chapter in his career as the CEO of a startup working with Data Monetization.

Here are my key takeaways:

Data Skills for the Future

  • As a leader in the data domain, you need to be a storyteller, to tell the story about the necessity of data, like quality or governance.
  • The Job titles for Data Scientist, Data Engineer, etc stayed consistent, but what we expect from someone with that job title changed greatly through the last years
  • Hard skills in data are not as important as they where for every company
  • Make sure you know, what you are optimizing for in your career
    • Are you optimizing for flexibility, self-employed consultant is best
    • Are you optimizing for building a legacy, be an entrepreneur
    • Are you optimizing for leading people and see people grow, become a line manager
  • Dont become a manger if your passion is within engineering. You will need to optimize your time for coaching people, not working on problem solving as an engineer.
  • «The technology of applying AI and ML becomes more and more simple and becomes more and more commoditized.»
  • Dont hire Data Scientist to build models that you can buy out of the box.
  • Don’t hire Data Scientist if you need Data Analysts. They work entirely different and the work is not comparable.
  • If you hire a Data Scientist before having good Data Engineers, then the Data Scientist cannot create any value
  • «In order to be successful as an engineer, you need to have a really transformative mindset.»
  • You need to enjoy the learning process, if not focus on something else in the IT-domain
  • Adopt an agile mindset. Agile fundamentals are key to todays work life.
  • «You need to embrace to be able to incubate things.» Build incubator squads as soon as a good idea pops up.

Recruitment

  • In a small company you need to be much more flexible and broader in the way you tackle problems, than in a larger company where you can be more specialized
  • As a hiring manager, don’t lean too much on the titles, but make sure you understand what you need in your company. This is key to writing a good add and attracting the right talent
  • In a job advertisement be specific: What does it mean to be a Data Engineer in the context of your business?
  • What is important for you as a company today, based on the trends coming?
  • Building code has become so much simpler. Do you still need developers that need to know all the details about a certain language?
  • Maybe a person that can be close to the business, and not so deep in programming can add more value?
  • «You have to know what it is you are optimizing for.» If you have an extremely complicated technology stack you need deep knowledge, if not don’t hire it.
  • In a recruitment process, focus on soft skills of rapid learners that can adjust to new situations and having an interest in understand your business use cases.
  • «My interview secret: Share a whiteboard session with me»
    • Try to figure out how self-sufficient a candidate can be in understanding how the business works and where to get relevant data
    • Test how candidates react in uncomfortable situations, with customers that are not always happy about results and solutions.
    • Look for candidates that show resilience in new and uncomfortable situations
  • Career models should be technology agnostic
  continue reading

68 つのエピソード

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