Data Scientist

We're looking for someone with a deep interest in data and analytics and the dedication to apply that passion. We need someone that possesses the technical ability to write modular, packaged code and implement data science methods for a diverse set of applications, as well as uncover insights from the models and communicate them concisely and clearly to stakeholders at varying levels of technical sophistication. The role also requires an observant attention to detail, the ability to work well on a small team, and a self-starter approach to problem-solving and upskilling.

As a Data Scientist, you will:

• Work closely with team members on design and execution of experimentation efforts, contributing to internal analytics libraries and integrating statistical theory
• Collaborate on prototyping new data science methodologies, including Synthetic Data, by translating needs identified with stakeholders into analytic frameworks that can be built into scalable user-facing tools
• Build, maintain, and enhance existing codebases
• Synthesize academic research, tech innovations, and cloud scalability, into R&D that elevates business value for clients, both current and prospective
• Validate AI and other analytics innovations from marketing science and data science teams globally
• Provide consulting and validation for internal teams and clients on areas ranging from statistical principles to sampling theory and data hygiene to LLM implementation

Requirements:

• High proficiency writing Python and/or R, including the ability to produce and maintain reusable and modular codebases (beyond scripting and notebooks)
• Bachelor's degree in Statistics, Math, Data Science or related
• Desire to work in a highly collaborative, fun, consensus-oriented environment.
• Experience in analytics, extracting and surfacing value from quantitative data
• Familiarity with LLM internals, prompting, agentic frameworks, as well as other generative AI models (e.g. tabular diffusion, GANs, VAEs)
• Ability to lucidly communicate data science concepts verbally and in written form

Pluses:

• Experience with Synthetic Data (i.e. Data Boosting/Augmentation, Digital Twins, Persona Bots, AI Respondents/Panels).
• Professional or academic experience with modern techniques and algorithms in machine learning and statistical computing such as generative models, neural networks, genetic algorithms, Bayesian modeling, data fusion, drivers analysis, clustering, predictive analytics, network analysis, Monte Carlo.
• Experience developing data science models in cloud platforms (e.g. GCP, AWS)
• Working knowledge of SQL and experience with database design and administration.
• Experience with collaboration tools (e.g. Atlassian suite) and version control systems (e.g. Git).
• Advanced degree (M.S., Ph.D.) but not required.
• Experience in the field of Market Research.