Omnicom Media’s Beauty Co-Lab is an Omnicom Media Group unit serving L’Oréal USA through data-driven media, commerce, and analytics solutions. The Senior Associate, Data Science will develop audience insights, apply machine learning and statistical modeling, build data tools, evaluate data sources, and translate quantitative analyses into actionable marketing strategies.
Exploring and experimenting with datasets to draw insights and search for client-specific and general solutions
Uncovering audience insights for clients:
Guiding non-technical stakeholders in choosing the right analysis to answer open-ended questions
Independently formulating and executing an analytics plan; iteratively building on client feedback to get to a satisfactory answer
Translating quantitative results back into actionable insights and interesting storytelling
Hands-on machine learning and statistical modeling for audience segmentation/extension
Collaboratively ideating and building tools that process, visualize, and analyze data to share across the organization
Evaluating new data and tools; quickly learning to navigate new platforms; extract, transform, and load data from various partner cloud storage solutions
Documenting and sharing knowledge with a wide variety of L’Oreal & BCL teams
Qualification
Required
1 to 2 years of relevant experience
Bachelor's degree in statistics, mathematics, economics, engineering, information management, social sciences, or business/marketing related fields
Must have hands-on expertise in SQL and Python
Experience with cloud computing such as Google Cloud Platform or AWS
Preferred
Advanced quantitative degree or intensive bootcamp experience is preferred
Benefit
Health insurance
Vision insurance
Dental insurance
401(k)
Healthcare Flexible Spending Account
Dependent Care Flexible Spending Account
Vacation days
Sick days
Personal days
Paid parental leave
Paid medical leave
STD/LTD insurance benefits
This role is hybrid, requiring three (3) days per week in the office. The remaining two (2) days may be worked remotely.
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