Data Science Analyst - Marketing amp Sales - University Students

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Data Science Analyst - Marketing & Sales - University Students
This posting is for University Students who are currently enrolled to matriculate from an Undergraduate or Advanced Degree program. If you are not currently a student, please consider our Experienced Professional role postings instead.
Who You'll Work With
You will be based in one of our North American office as part of our Marketing Analytics team. This team helps clients transform their marketing and sales by applying advanced analytics to generate value-creating insights from big data. You will collaborate with colleagues and clients to create new strategies across a wide platform of projects, from customer life-cycle management, MROI, pricing and promotions, marketing mix modeling, to analytic transformation.
What You'll Do
You will help expand our current analytics capabilities and architect new strategies and applications within a dynamic innovative organization.
In this role, 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, with an eye on optimizing every aspect of our clients' marketing practices.
Through the measurement, manipulation, reporting and dissemination of broad sets of data, you will create valuable, transformative business strategies. You will apply and advise teams on the 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.
As a core member of the consulting team, you will conduct deep analytics on a broad set of client and external data and play a lead role in team problem solving through findings and insights from that analysis.
Qualifications
Student in the final year of undergraduate or advanced degree in a STEM related field (e.g., operations research, statistics, applied math, engineering, business analytics) with graduation date of Spring/Summer 2022
Deep understanding of statistical and predictive modeling concepts, machine-learning approaches, clustering and classification techniques and recommendation and optimization algorithms
Demonstrated proficiency in Python and SQL; additional knowledge of other data mining software (Spark, Scala, etc.) is a plus
Ability to easily understand complex algorithms and logic to process data
Experience working with a large volume of data with the ability to solve performance issues
Basic business intuition and clear expertise in analyses; ability to describe analytic processes including when and why specific approaches are favored
Excellent communication and presentation skills; ability to visualize and report insights creatively in variety of formats to various stakeholders
COVID-19 vaccination mandate:
Employment with McKinsey & Company, Inc. in the United States and Canada is conditioned on proof of full vaccination against the COVID-19 virus (with a WHO-approved or Health Canada-approved COVID-19 vaccine, as applicable) or approval of an exemption due to a qualifying medical condition or sincerely held religious belief prior to start date.
FOR U.S. APPLICANTS: McKinsey & Company is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by applicable law.
FOR NON-U.S. APPLICANTS: McKinsey & Company is an Equal Opportunity employer. For additional details regarding our global EEO policy and diversity initiatives, please visit our and sites.
Job Skill Group - CSS Pre-Associate
Job Skill Code - AF - Analytics Fellow
Function - Marketing & Sales;Technology
Industry -
Post to LinkedIn - #LI-DNI
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Equal Opportunity Employment Disclaimer
McKinsey & Company is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, national origin, disability, veteran status, and other protected characteristics.

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