McKinsey & Company
- Undergraduate degree in maths, statistics, physics, engineering required
- Up to 4 years of professional experience in programming; experience applied to business problems is a plus
- Programming (focus on machine learning) R and/or Python (must), SPSS, SAS, Ruby, Hadoop (valued)
- Data treatment/Data mining, e.g. SQL, AWK, Access, Spark, Excel (valued)
- Statistical knowledge is a plus
- Demonstrated aptitude for analytics
- Proven record of leadership in a work setting and/or through extracurricular activities
- Ability to work collaboratively in a team environment
- Ability to work effectively with people at all levels in an organization
- Skills to communicate complex ideas effectively
- Ability to communicate complex ideas effectively – both verbally and in writing – in English and the local office language(s)
- Knowledge of other languages including Portuguese, German, French and Italian will be valued
Who You’ll Work With
You’ll work with our Analytics´ team in Madrid, focusing on machine learning. This global practice supports clients in many different industries facing challenges of developing and implementing tailored concepts for prediction and prescription.
What You’ll Do
You will focus on the development of advanced analytics models to optimize underlying business problems.
As a Data Scientist, you will shape the future of what data-driven organizations look like, drive processes for extracting and using that data in creative ways, and create new lines of thinking within an infinite number of clients and situations.
You will create valuable, transformative business strategies through the measurement, manipulation, reporting and dissemination of broad sets of data. You will apply and advise on state-of-the-art advanced analytic and quantitative tools and modeling techniques in order to derive business insights, solve complex business problems and improve decisions. You will review, support and advise on the day-to-day analytics requirements of clients’ key operational processes while continually improving the impact of these processes.
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