Data Scientist – McKinsey Analytics

McKinsey & Company

Qualifications

  • MSc or PhD degree in the field of Computer Science, Machine Learning, Applied Statistics, Econometrics, Mathematics
  • 2+ years of experience in statistical modelling and/or machine learning
  • 2+ years of technical experience in predictive analytics, machine learning, optimization, and fluent in a number of the following technologies: R, Python, SQL, MATLAB, SAS, Java, Spark, Scala, AWS, Tableau
  • Experience in applying data science methods to business problems
  • Ability to explain complex analytical concepts to people from other fields
  • Strong presentation and communication skills
  • Fluency in English together with fluency in either French or Dutch is mandatory

 What you’ll do

You will leverage a solid understanding of business trends, issues, and concepts to efficiently respond to complex research questions.

You will deliver synthesized, actionable facts and insights to our consulting teams and clients.  You will also have the opportunity to work alongside senior knowledge professionals on larger client projects and internal knowledge initiatives. By collecting and analyzing data and information found in databases and/or other primary and secondary research tools, you will assemble a fact base and counsel consultants and clients on the scope of the business and economic trends surrounding the issue. You will also provide insights and analysis for solving client problems at hand. You will work towards enhancing the practice’s current awareness of development in the energy sector.

Your day-to-day tasks will include identifying the key issues for the problem at hand and determining the most appropriate solution while effectively balancing quality, availability, timeliness, and cost factors. You’ll perform a variety of analyses such as benchmarking, trend identification, industry profiling, market sizing, growth projections and  opportunity scanning in order to add value to problem-solving discussions.

You’ll provide synthesis, insight and client implications within tight deadlines via end products in the form of PowerPoint charts, written memos, models and framework. In time, you will also participate in, and eventually lead, internal knowledge development and capability-building initiatives and you’ll use advanced analytics tools to prepare and analyze large datasets to distill insights.