Manager, Advanced Analytics - Market Insights - L'Oreal Corporate Digital & Marketing Office

JOB TITLE: Analytics Manager, Advanced Analytics – Market Insights Team REPORTS TO: AVP, Advanced Analytics – Market Insights Team DIVISION: Market Insights Team, Consumer Function – US CDMO LOCATION: New York, NY Who We Are: For more than a century, L’Oréal has devoted its energy, innovation, and scientific excellence solely to one business: Beauty.
Our goal is to offer each and every person around the world the best of beauty in terms of quality, efficacy, safety, sincerity and responsibility to satisfy all beauty needs and desires in their infinite diversity.
CDMO: The Corporate Digital and Marketing Office mission is to put the consumer at the heart of L’Oréal’s business and drive digital innovation.
As a force for L’Oréal’s innovation, CDMO delivers the best consumer experiences, drives new marketing models, and spearheads new digital capabilities to futureproof the group’s business.
What You Will Do: The Analytics Manager is someone who is passionate about navigating the ambiguous landscape of measurement at Loreal.
The responsibility of this role is two folds: 1.
Using the in house tools to run various simulations to understand the impact of media spend on sales 2.
To continue development and enhancement of Loreal’s suite of analytical tools (including but not limited to MMM, A/B testing etc.
) Understand the saturation curve outputs from the MMM’s and ensuring that those outputs pass both statistical as well as business validations.
Develop a critical understanding of the parameters that go into modeling the ad stocks and saturation curves and help ensure that the parameters being fed into the model meet business requirements.
Assist in scenario planning – Using the in-house simulation tool, work with brand teams to aligns on different budget requirements and run various scenarios to identify the optimal ones.
Work with the brand/media teams to work them through the findings and implications.
Iterate through scenarios to align on the ones that meet brand objectives.
Assist in the development of in-house modeling capability.
Iterate through and continuously enhance the in house MMM currently being built in Python on our GCP platforms.
Enhance Loreal’s A/B testing capability by iterating on our tools.
Bring an “outside-in perspective” to analytic development; explore, identify, and assess external best practices, and translate them to the relevant applications internally.
Work with stakeholders to improve current analytic processes and develop new capabilities that set us apart in the marketplace.
Constantly look for ways to improve the usage of L’Oréal’s existing data and analytics infrastructure.
Maintain collaboration with third party measurement partners (e.
g.
, Nielsen, Ekimetrics) What You Will Learn: The role is a mix of application of Data Science as well as building of the tools.
It offers a unique experience to both build the models as well as use it to provide strategic recommendations.
This role is an excellent transition role from a pure technical engineer to one that gives you a chance to learn more about the media planning and dictate how the work that is done is consumed and used.
What we are looking for: First and foremost, we love people that are curious, collaborative, eager to have an impact and who value innovation, autonomy, and team spirit.
This will be a very hands-on role in both building models as well as using tools to build scenarios, hence a willingness and interest in learning new tools and capabilities is a must.
In terms of expertise the must haves are: Experience in the Advanced Analytics realm, preferably in media, marketing or adjacent field.
Hands on experience in building MMM/MMX/Price and Promotion models/Forecasting models models at a Data Science vendor will be extremely useful.
Experience in building models in Python/R with exposure to pandas, numpy, scikit, Pytorch.
A good background in SQL Working knowledge of GCP/Vertex AI (or its equivalent counterparts in other systems) In terms of nice to have: A background in Bayesian statistics (either through coursework or practical work experience) Background in hypothesis testing - When to use what kinds of tests.
Any prior background in Linear optimization/functional optimization etc.
(or exposure through using tools such as lpopt) EDUCATION/EXPERIENCE: BA/BS in Statistics, Mathematics, Economics, Computer Science Strongly preferred MS in Statistics/Data Science 4+ years of analytical industry experience Brand, Retailer, Data science vendors a plus CPG experience a plus What’s In It For You: Salary Range: $100,400 – $143,000 (The actual compensation will depend on a variety of job-related factors which may include geographic location, work experience, education, and skill level) Competitive Benefit Package (Medical, Dental, Vision, 401K, Pension Plan) Hybrid Work Policy (3 Days in Office, 2 Days Work from Home) Flexible Time Off (Paid Company Holidays, Paid Vacation, Vacation Buy Program, Volunteer Time, Summer Fridays & More!) Access to Company Perks (VIP Access to L’Oréal’s Internal Shop for Discounted Products, Monthly Mobile Allowance) Learning & Development Opportunities (Unlimited Access to E-learnings, Lunch & Learn Sessions, Mentorship Programs, & More!) Employee Resource Groups (Think Tanks and Innovation Squads) Access to Mental Health & Wellness Programs Don’t meet every single requirement? At L'Oréal, we are dedicated to building a diverse, inclusive, and innovative workplace.
If you’re excited about this role but your past experience doesn’t align perfectly with the qualifications listed in the job description, we encourage you to apply anyways! You may just be the right candidate for this or other roles! We are an Equal Opportunity Employer and take pride in a diverse environment.
We would love to find out more about you as a candidate and do not discriminate in recruitment, hiring, training, promotion, or other employment practices for reasons of race, color, religion, gender, sexual orientation, national origin, age, marital or veteran status, medical condition or disability, or any other legally protected status.
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