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Who is the data scientist the market is searching for?

Who is the data scientist the market is searching for?

There have been analyzed for that 90% of the different job offers* for the Data Scientist placed on platforms as Glassdoor, LinkedIn...

IN  ORDER  TO  UNDERSTAND  THE  PROFILE  OF  THE  DATA  SCIENTIST  THE  MARKET  (by  market  we  understand     HR     managers     and     recruiting     specialists as the main agents in charge to hire the right professionals for business as well as the business leaders who define the requirements) is searching for, we  analyzed  90%  of  the  different  job  offers*  for  the  Data Scientist placed on such platforms as Glassdoor, LinkedIn and slb.dejobs.org (February – March 2020).

*These  job  offers  are  profiles  of  the  Data  Scientist  searched by the companies that are leaders in different industries such as analytics, software and engineering, energy,     consumer     goods,     banking,     information     technologies, tobacco.

The  goal  was  to  understand  the  profile  of  the  so  called  “Market  Data  Scientist”  (the  ideal  candidate  for the market) through the following 5 categories:

1) “Market Data Scientist” - Education requirements

2) “Market Data Scientist” - Tools and technologies capacity requirements

3) “Market Data Scientist” - Business acumen requirements

4) “Market Data Scientist” - Soft skills and foreign languages requirements

5) “Market Data Scientist” - Professional activities requirements

Having this information, we were be able to define:

  • WHOistheidealDataScientistthemarketissearching for;
  • if the market “mission” to find the ideal candidate is possible;
  • and how companies can apply Crystal Pragmatic Approach to BUILD and SELECT Data Scientist Talents.
  1. MARKET DATA SCIENTIST”

EDUCATION REQUIREMENTS. The  bar  chart  (Figure  1)  illustrates  the  education  domains  preferred  by  the  HR  for  the  Data  Scientist  candidate. It can be seen that the Computer Science Master degree is required by the 92% of the market searchers,   and   then   followed   by   the   Math   and   Statistics graduates (83% and 58% respectively). 

It is worth noting that some of the employers in their announcements do not specify clearly the education domain   of   the   Data   Scientist   searched   for   their   business.

Such  statements  as  “any  quantitative  field”,  “other  technical   field”,   “other   quantitative/computational   discipline”,  “another  field  relevant  to  Data  Science”,  etc.  may  puzzle  the  potential  work  searchers  and  might be considered as a market confusion.

The  diagram  (Figure  2)  shows  how  the  education  domains  for  the  Data  Science  professional  overlap  and   the   size   of   the   connecting   part   –   the   ideal   education set for the Data Scientist.

2)MARKET DATA SCIENTIST

TOOLS AND TECHNOLOGIES CAPACITY. Summarizing  the  information  regarding  tools  and  technologies requirements (Figure 3), Python and R are obviously “champions” of search within 83% and 67%  HR  managers  respectively,  and  it’s  clear  as  these   tools   provide   very   neat   means   for   data   collection,  modelling  and  visualization  stages  of  a  data Science Project.

Remarkable,  that  in  addition  to  the  most  popular  tools and technologies represented in the charts, the market is searching also the candidate to have some knowledge  in  Machine  Learning  technologies  such  as  Tensorflow, Theano, MXNet, PyTorch, and some of them –  in in data mining software (e.g. Weka, Knime, Databricks, Tensorflow, Scikit-learn).

Some  employers  ask  for  experience  with  such  Data  Science libraries like Pandas, Scikit-Learn, NumPy, etc.

The    diagram    (Figure    4)    illustrates    how    the    overlapping    of    the    most    popular    tools    and    technologies    required    from    the    Data    Science    professional  by  the  HRs  leaders–  the  ideal  tool/technology set for the Data Scientist.

3)MARKET DATA SCIENTIST

BUSINESS ACUMEN REQUIREMENTS. If  to  sum  up  the  experience  requirements  for  the  Data Scientist (Figure 5), the ideal candidate should have 3-5 years’ experience in the industry or at least poses   some   business   knowledge   as   he   should   provide C-suit with business insights and be able to communicate results to business community.

Business   Acumen   (as   a   possession   of   business   knowledge   in   a   specified   area)   is   in   the   top   3   experience   requirements   for   the   Data   Scientist   searched by the market.

Meanwhile,   the   ideal   candidate   should   have   a   working   background   by   using   ML   technologies   (required   by   67%   of   the   employers)   and   have   sufficient  proved  skills  in  programming  (needed  by  58% of the companies).

4) MARKET DATA SCIENTIST

SOFT SKILLS AND FOREIGN LANGUAGES REQUIREMENTS. 75%  of  the  employers  require  the  Data  Scientist  to  be  able  to  communicate  effectively  within  internal  departments as well as to be a team player, and 50% of  them  expect  the  candidate  to  have  leader  skills  and problem-solving mind-set (Figure 6).

Moreover  100%  of  employers  need  a  Data  Science  professional   with   excellent   written   and   verbal   English,   and   good   understanding   of   a   second   language   such   as   German,   Italian,   or   French   is   preferred by 30% of the employers.

5)MARKET DATA SCIENTIST

PROFESSIONAL ACTIVITIES REQUIREMENTS. By      professional      activities      requirements      we      understand   the   professional   responsibilities   and   expectations   from   the   Data   Scientist   employee.   Analyzing  the  job  offers,  most  of  the  employers  require the Data Scientist to be able to master the full spectrum of the Data Science Life Cycle and possess a  level  of  flexibility  and  understanding  to  maximize  returns at each phase of the process (Figure 7).

Summarizing the main requirements from the search categories from above, we can highlight the following key   requirements   from   the   Ideal   Data   Scientist   candidate hunted by the HRs:

  • Master degree in Computer Science, Mathor Statistics (and preferably PhD)
  • Strong knowledge and working experience (3-5years) with Python and/or R,andknowledge of Machine Learning technologies and tools
  • High level of business understanding (energy, construction, accountancy, banking, other businesses depending on the sector of employment), as the candidate should provide business managers with business insights and prepare data for strategic decision making
  • English is a must speak language,and a second language such as French, Italian, Spanish or German is required fo rpositions where candidate should communicate with foreign departments
  • Teamwork, leadership and problem-solving mind set
  • We can continue with the list, .... but the question that appears is do you really expect to find all these knowledge and skills in one person?

The overlapping part of the diagram (Figure 8) shows us  the  approximate  connecting  point  of  the  category  requirements from the Ideal Data Scientist. In Crystal we believe that to find such a Data Scientist Candidate is almost impossible, as a set of knowledge and  skills  for  the  ideal  candidate  is  incompatible.  Thus,  market  attempts  to  search  and  find  the  ideal  Data  Scientist  could  be  described  in  some  way  as  Mission: Hunting a Data Scientist = Hunting a Unicorn? 

That  is  why,  at  Crystal,  we  developed  our  approach  how to build and select the Data Scientist Talents.

CRYSTAL PRAGMATIC APPROACH TO BUILD AND SELECT DATA SCIENTIST TALENTS

CRYSTAL  PRAGMATIC  APPROACH  TO  SELECTDATA SCIENTIST TALENTS - is a set of pragmatic recommendations for business how to attract a right DS candidate.

Crystal  Pragmatic  Approach  to  select  Data  Scientist  Talents is based on the following principles:

1) DATA SCIENCE PROJECTS as multidisciplinary projects looking at specific business issue/problems (please  note:  we  start  with  business  problem  not  with tech)

2) DATA    SCIENCE    PROJECTS    ARE    HIGHLY    COLLABORATIVE   WITH   A   DIVERSE   EXPERT   TEAM,  including  the  following  disciplines  and  key  roles:

  • BUSINESS EXPERTISE – “The Business Guy”, it may  be  an  accountant,  financial  analyst,  engineer,  marketing  manager  or  other  business  expert  who  has a solid understanding of the business processes and preferably with the academic background in the business domain, and who has a system view of the business problem;
  • MATHEMATICS&STATISTICS  –  “The  Data  Nerd”, a professional with profound knowledge and good skills in building, implementing and developing predictive and prescriptive models, and with a strong fundamental    understanding    of    various    modern    machine-learning methods;
  • COMPUTER SCIENCE / IT - “The Hacker”, it may be   a   Data   Developer/Software   Engineer   or   other   professional    with    experience    in    Big    Data,    ML    technologies, general purpose programming languages, and who is a tech solver of the business issue.
  • COMMUNICATION  –  “The  Communicator”,  it  may  be  a  Data  Analyst  or  a  business  expert  with  strong  oral  and  written  communication  skills,  and  who  is  able  to  communicate  sophisticated  concepts  to intelligent business community.

3) Adopting  a  new  approach  from  searching  the  candidate   to   SELECT   THE   DATA   SCIENTIST   TALENT  BY  PLACING  THE  PRAGMATIC  JOB  OFFER. Once the employer defines the Data Science Project   with   the   business   problem   at   its   core   (principle 1), he can identify the team players needed with  the  targeted  Data  Science  candidate  and  thus  place    the    Pragmatic    Job    Offer    that    is    highly    personalized and attractive for the right Talent.

By  using  the  Crystal  Pragmatic  Approach  to  select  Data Scientist Talents, companies will be able to shift the  paradigm  from  searching  a  Unicorn  to  selecting  and  attracting  a  targeted  Data  Scientist  Talent,  as  they will start considering the process of “doing Data science”  as  an  interdisciplinary  project  and  thus  recruit a right candidate for the Data Science team.

CRYSTAL  PRAGMATIC  APPROACH  TO  BUILD  DATA  SCIENTIST  TALENTS  –  is  an  educational  methodology    based    on    more    than    15    years’    experience  to  build  Talents  through  attracting  and  selecting  them  from  the  best  public  universities,  then   educating   Talents   in   Crystal   Academy   via   professional courses and project trainings, followed by further service delivering.

Focusing on a market problem-solving and basing on its educational expertise, Crystal System is planning to  implement  the  Data  Scientist  course  (DSC)  with  its  partner  universities,  made  in  collaboration  with  the  best  domain  experts,  aligned  with  the  market  business requirements and Crystal System partners, and it is supported by the market research.

The   Crystal   System   DSC   is   aimed   to   build   and   accelerate  Talents  career  in  Data  Science  providing  them with the world-class training and skills required to   become   successful   in   this   domain,   as   well   as   prepare the industry-ready professionals and teams.

The  Crystal  DSC  is  focused  on  both  market  and  Talents:  by  deep  diving  Talents  into  the  nuances  of  data         interpretation,         mastering         powerful         programming  skills  and  focusing  on  the  business  acumen as an essential element, and helping market by building industry-ready professionals (Figure 9).

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CRYSTAL SYSTEM GROUP