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The Data Scientists Hub

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Data Scientists Recruitment & Training Services

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What challenge do we face while searching for data scientists?

The shortage of data scientists is becoming a serious constraint in Africa. The main challenge for managers is to learn how to identify that talent, attract it to an enterprise, and make it productive. None of these tasks is as straightforward as it is with other, established organizational roles. There are no university programs offering degrees in data science. There is also little consensus on where the role fits in an organization, how data scientists can add the most value, and how their performance should be measured.

STEP 1 - STRATEGY

  • We a build data science talent sourcing strategy.

    • STEP 2 - PHISHING

  • Automated public profiles phishing.
  • Database amalgamation.
  • Apply candidate miner algorithm.

    • STEP 3 - SCREENING

  • Telephone-screening.
  • Candidate, one on ones.
  • Technical testing.
  • The ideal data science test.

    • STEP 4 - CUSTOMER REVIEW

  • Forward best options to the customer.

    • STEP 5 - BASIC FOLLOW UP TRAINING

  • Help the candidate undesrtand how to approach the tasks ahead.

    • What do data scientists do?

    They make discoveries while swimming in data. They should be able to bring structure to large quantities of formless data and make analysis easy. They identify rich data sources, join them with other, potentially messy data sources, and clean the resulting set. They build dashbboards and amazing visualizations. They build and automate predictive models. As they make discoveries, they communicate what they’ve learned and suggest its implications for new business directions. Often they are creative in displaying information visually and making the patterns they find clear and compelling. They advise executives and product managers on the implications of the data for products, processes, and decisions.

    How do they compare to traditional data analysts?

    I would say, data scientists are smart data hackers, data analysts and great communicators. The combination is extremely powerful—and rare in Africa.

    What skills do they possess?

    (1) Ability to write code. (2) Great story telling. (3) They are hyper curious - that means, they can create situations that identify unforseen events that will actually happen. (4) Have theoretical and analyticsl reasoning. (5) Solid foundation in maths, statistics and computer science.

    What to watch for?

    It’s important to keep that image of the scientist in mind—because the word “data” might easily send a search for talent down the wrong path. As Portillo told us, “The traditional backgrounds of people you saw 10 to 15 years ago just don’t cut it these days.” A quantitative analyst can be great at analyzing data but not at subduing a mass of unstructured data and getting it into a form in which it can be analyzed. A data management expert might be great at generating and organizing data in structured form but not at turning unstructured data into structured data—and also not at actually analyzing the data. And while people without strong social skills might thrive in traditional data professions, data scientists must have such skills to be effective(hrb.org).

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