Liftlab is the leading provider of science-driven software to optimize marketing spend and predict revenue for optimal spend levels. We call this the Science of Marketing Effectiveness. Our platform combines economic modeling with specialized media experimentation so brands and agencies can clearly see the tradeoffs of growth and profitability.
With decades of experience in marketing analytics and data science, our team of industry experts and thought leaders is proud to enable leading and emerging brands such as Cinemark, Express, Hanna Anderson, Lulu & Georgia, Pandora, Sephora, Skims, Tory Burch, Thrive, and Vionic, with our cutting-edge solutions and strategic guidance.
LiftLab is looking for a highly organized, analytical professional who loves to interact externally with clients and internally across teams. Every day there is new data from our experiments and AMM (Agile Mixed Model) to analyze and we’re seeking someone who is eager to question these results and craft a story to relay directly to their roster of clients. The ideal candidate will have a robust understanding of statistics, coupled with previous experience in the marketing industry. This should include a comprehensive knowledge of platforms and investment strategies, such as auction systems, audience targeting, and related areas. The ability to comfortably interpret analytical models to make tactical recommendations for media optimization is key to this role. The Marketing Science Analyst will report directly to the VP Marketing Science & Product Marketing and work alongside our cofounder, John Wallace.
Provide the client with actionable insights from model and experiment results like recommended budget allocation, new experiments to run, or interesting days in the data to investigate further.
Conceptual understanding of econometric models, along with the ability to develop informed hypotheses and intuitively verify their correctness.
Set up test designs that address the questions clients are looking to answer.
If an experiment has unexpected results, ask questions about assumptions that were made, hypothesize potential problems, do basic exploratory analysis to get to the root cause.
Participate in high level, theoretical discussions with our advisory board to push LiftLab’s offerings.
Coordinate with the marketing science team and global data science teams to ensure timely delivery of projects, while also contributing to a more comprehensive and in-depth research analysis.
Package and deliver client feedback to the engineering team to inform new product features.
Quickly gain understanding on how all experimentation/AMM models conceptually work. Understand the inputs and outputs of the models, what sort of factors would impact them both, and make recommendations for modifications.
Hands-on experience in constructing measurement frameworks that merge descriptive analytics, modeled insights, and causal inference to provide tactical recommendations for media optimization. This includes the strategic orchestration of multiple data sources into a straightforward and unified path forward.
5+ years in an analytical role, preferably in the field of marketing.
Ability to have informed, thoughtful conversations with C-level executives, data scientists, and customer success managers.
Experience with attribution platforms for both digital and linear media, the data required to power them and methodologies for measurement.
You should be able to:
Break down complex ideas into smaller parts until a wide ranging audience is able to understand.
Thrive in fast moving environments that require context switching between multiple client meetings, experiment results, and internal meetings.
Highly organized and good at streamlining processes to help the internal team execute more efficiently.
Have a strategic mindset with a strong understanding of utilizing data to solve high-stakes problems in the marketing space.
Be a detective who doesn’t take unexpected results or strange data at face value. Ask questions, look at new data, run new models, until they have turned over every rock.
Understand causal analysis/econometric modeling/media investment strategies and what the metrics actually mean.