Applied Scientist III

Pay rate range - $70/hr. to $72/hr.
Fully onsite

Must Have
1. Causal Inference
2. Experiment Design
3. Cloud Computing Experience

Basic Qualifications

• PhD, or Master's degree and 4+ years of experience in CS, CE, ML, Statistics, Economics, or related field
• 3+ years of experience building machine learning models or developing algorithms for business application
• Experience with causal inference methods (e.g., A/B testing, difference-in-differences, instrumental variables, propensity scoring, synthetic controls)
• Familiarity with cloud computation (e.g., AWS tools such as S3, SageMaker, EMR, or EC2)
• Experience programming in Python, R, or related language

Preferred Qualifications

• PhD in Statistics, Economics, Computer Science, Machine Learning, Operations Research, or equivalent quantitative field
• 5+ years of experience building marketing measurement models (MMM, CLV, multi-touch attribution)
• Experience designing and analyzing A/B tests and geo experiments at scale
• Experience in patents or publications at top-tier peer-reviewed conferences or journals (e.g., KDD, ICML, NeurIPS, QME, Marketing Science)
• Excellent oral and written communication skills, with the ability to communicate complex technical concepts to all levels of the organization

Key Job Responsibilities
•                Marketing Measurement: Develop and enhance Marketing Mix Models (MMM) to measure channel effectiveness, optimize budget allocation, and quantify ROI across marketing investments.
•                Customer Lifetime Value (CLV): Build and refine CLV models to inform acquisition targeting, retention strategies, and customer segment prioritization.
•                Causal Inference & Experimentation: Design, execute, and analyze A/B tests and quasi-experiments (e.g., geo holdouts, difference-in-differences, synthetic controls) to measure incremental lift of marketing campaigns.
•                Campaign Optimization: Partner with marketing teams to translate measurement insights into actionable recommendations for targeting, creative, and channel mix.
•                Cross-functional Collaboration: Work with data scientists, machine learning engineers, and business stakeholders to deliver measurement solutions to production.

About Project
The Marketing Science team applies scientific methods and research techniques to enhance our understanding of AB consumer behavior, market trends, and the effectiveness of marketing strategies.
Our goal is to develop and advance theories and models that can be used to make informed decisions in marketing and to provide insights into consumer decision-making processes.
Additionally, we seek to identify and explore emerging trends and technologies in marketing, and to develop innovative approaches for addressing the challenges and opportunities in the field.