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Vaga de Data Scientist
McKinsey & Company em Rio de Janeiro - RJ

Descrição da Vaga

Data Scientist - University Students

Who You'll Work With

You will work with an Advanced Analytics team in one of our global offices. Blending strategic thinking with hands-on practicality, our teams of consultants and experts work to develop and implement complex analytics strategies that solve our clients' most critical problems.

Our teams are made up of analysts, mathematicians, data scientists, designers, software engineers, product managers, client development managers and general managers with a base of expertise in a function or industry. Data Scientists are staffed on engagements or product teams and are expected to leverage expertise to solve some of the most pressing and complex issues at clients.

Please review the team options and locations below to clarify your application preferences.

McKinsey Analytics– (ACRE- Denver, Waltham; Financial Services Lab- Waltham; Geospatial - Waltham; Healthcare Analytics & Delivery- New York, Waltham; Ingenuity- Waltham; Journey Analytics- Waltham; People Analytics- Waltham; Pharma and Medical Products- New York; Public and Social Sector- New York, Waltham, Washington DC)

You will apply an analytical, entrepreneurial mindset to foster innovation driven by analytics, design thinking, mobile and social by developing new products/services and integrating them into our client work

Operations Advanced Analytics - ( Silicon Valley, Chicago, Boston, Bogota, Buenos Aires, Lima, Panama, Rio de Janeiro, Santiago, Sao Paulo)

You will apply your data science toolkit across our operational service areas (ex. Supply Chain, Product Development, Manufacturing, etc.) to devise creative approaches for our clients in areas such as inventory management, predictive maintenance, or logistics network design

Risk Advanced Analytics - ( Waltham, Silicon Valley)

You will complete targeted quantitative analyses, build components of advanced models, and apply machine learning algorithms in different fields of risk (strategic risk management, operational/fraud, credit and market risk, pricing, asset evaluation and loss mitigation)

What You'll Do

You will perform statistical data analyses, data mining and optimizations using multiple tools and techniques to get insights from large complex data sets.

In this role you will conduct hands-on rigorous quantitative analysis, including getting the data, cleaning it (when necessary / relevant), and exploring it for accuracy. You'll deploy statistical modeling and optimization techniques most suited for the business problem (using R, Python, SQL, and/or other relevant tools). With support, you'll advise client teams on analytic methodologies and approaches to address their specific needs, including discussing data collection, architecture, associated costs and trade-offs, and recommendations.

You'll interpret outputs of statistical models and results to translate input from quantitative analyses into specific and actionable business recommendations and implications. This includes providing detailed documentation of modeling techniques, methodologies, assumptions made and process steps.

You'll manage delivery of analytical solutions to client via written and verbal presentations, including capability building as appropriate. You may advance McKinsey’s overall knowledge base by providing analytical rigor and problem solving to our proprietary knowledge investments.

You'll begin to own an analytics work stream, manage respective tasks and client project team members/experts, while building client relationships. You'll support analytics recruiting efforts (e.g., attends recruiting events, participates in WebExes, etc.)


  • University student in their final year in a STEM related field (Ops Research, Statistics, Applied Math, Engineering, Business Analytics); expected graduation date of December 2018 or May 2019. Or, currently completing your first year in a non-business, 2-year masters program and have less than 2 years of work experience
  • Deep understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques, and recommendation and optimization algorithms
  • Experience in one or more programming languages (R, Python, C++, etc.)
  • Ability to easily understand the complex algorithm and logic to process data
  • Experience working with a large volume of data with ability to solve performance issues
  • Practitioner of statistical data quality procedures or test driven approach for quality assurance
  • Basic business intuition and clear expertise in analyses with the ability to describe analytic processes, including when and why specific approaches are favored
  • Excellent communication and presentation skills with an ability to visualize and report insights creatively in variety of formats to various stakeholders
  • Ability to deliver in deadline-driven environment
  • Team player with a passion for coaching colleagues and clients
  • Willingness to travel up to 80%
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