Data Science Summer Intern - FinLab

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

WHO YOU'LL WORK WITH

You will be based in our Waltham, Chicago or Dallas office as part of our FinLab team.

You will work with analytics colleagues, software engineers and architects, McKinsey consultants and clients to carry out complex analyses, build statistical and machine learning models, and develop actionable recommendations for a wide-range of strategic topics in industries such as financial services, real estate, retail and public sector.

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 quantitative analysis including getting the data, cleaning it (when necessary/relevant) and exploring it for accuracy. Additionally, you'll deploy statistical modeling and optimization techniques most suited for the business problem (using R, Python, SQL, and/or other relevant tools).

For 10 weeks over the summer, our internship program offers undergraduate students and eligible graduate students first-hand experience with digital and analytics consulting at McKinsey. You will participate in an introductory training program, as well as a range of social activities to get to know your office and the firm.

QUALIFICATIONS

• University student in a STEM related field (e.g. computer science, etc.) with expected graduation date between December 2021 to June 2022; master’s student completing the first year in a non-business, 2-year master’s program with less than 2 years of work experience

• Experience in two or more programming languages (R, Python, C++, etc.)

• Experience working with a large volume of data with ability to solve performance issues

• Deep understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques and recommendation and optimization algorithms

• Ability to easily understand the complex algorithm and logic to process data

• Ability to work collaboratively in a team environment and effectively with people at all levels in an organization

• Basic business intuition and clear expertise in analyses with the ability to describe analytic processes, including when and why specific approaches are favored

• Strong analytical and problem-solving skills paired with the ability to develop creative and efficient solutions
• Strong command of English language (both verbal and written)