Position

Senior Data Scientist – Marketing

Reports to

Data Science Manager

Summary

We are seeking a highly skilled and motivated Senior Data Scientist - Marketing to join our team. This role will be pivotal in directing the setup of our marketing data analytics infrastructure, focusing on attribution, ROI, web analytics, and experimentation. The ideal candidate will work closely with our data engineering team to develop and implement data science products that enable seamless and scalable data analysis for our marketing initiatives.

Responsibilities

Lead the design and implementation of data science products to analyze marketing performance with a specific focus on ROI. Identify and extract key data points to support attribution analysis and optimize strategies at every level of the marketing funnel. Collaborate with the data engineering team to design and recommend data structures that facilitate efficient data analysis for several foundational marketing functions: ROI, web analytics, and experimentation. Drives marketing strategy through effective execution of projects. Ensures data products drive decision making by demonstrating their efficacy and fostering a business understanding of value provided. Develop and maintain scalable data products and algorithms to support ongoing marketing analytics needs. Proactively identifies actionable solutions to business pain points or previously untapped opportunities. Builds and implements machine learning models to solve complex problems and optimize business performance. Presents, discusses and debates findings with stakeholders and leadership – including the Executive Team. Stays up to date with the latest trends and best practices in data science and marketing analytics. Mentor and guide junior data scientists and analysts within the team.

Requirements

6+ years of experience in data science, with a focus on marketing analytics. Expert-level ability programming in python and writing complex queries in SQL. Strong understanding and proven experience working with and developing: attribution models, channel and campaign-level ROI, and automated experimentation pipelines. Proficiency in the traditional machine learning lifecycle: data preparation, model selection, training, evaluation, implementation and continuous integration. Excellent problem-solving skills and attention to detail. Strong communication skills, with the ability to present complex data insights to non-technical stakeholders.

Stand-out assets:

MS/Ph.D. in Data Science, Statistics, Computer Science or related field. Demonstrated experience building an experimentation framework from the ground up. Experience working within the Google Analytics suite. Experience in MLOps, working with infrastructure teams to deploy machine learning models. Previous experience in the finance and/or nonprofit sectors.