Senior Marketing Data Scientist

Zepz•,

9h•Remote

About The Position

This is an individual contributor (IC) role, ideal for someone who thrives on solving complex problems, collaborating cross-functionally, and driving impact. You’ll be supporting key initiatives around Marketing Mix Modeling (MMM), attribution, and causal inference, helping the business understand the true impact of marketing and optimize our media investments accordingly.

Requirements

Bachelor’s or Master’s degree in Mathematics, Statistics, Economics, Data Science or a related field.
5-10 years of experience in marketing analytics or performance marketing measurement.
Expertise in Marketing Mix Modeling, attribution techniques, with demonstrated experience applying these to real-world problems.
Proficiency in SQL and data-visualisation tools.
Proficiency in Python, with demonstrated experience building open-source Marketing Mix Models (Robyn, Meridian, LightweightMMM etc.).
Deep understanding of the marketing funnel and channels.
Strong project management and stakeholder communication skills.
Ability to work independently and lead high-impact projects end-to-end.
Passionate about using data to solve marketing problems
Able to demonstrate tenacity and a willingness to go the distance to get something done.
You don't mind doing things manually but automate at every opportunity.
You are naturally inquisitive, intellectually curious and can make sense of complex systems or information.
You take a structured approach towards goals and pay attention to detail.
You can easily communicate with non-technical folks and translate their feedback into code.
You are comfortable defaulting to over-communication and overreaching when it comes to coordination.
You can adjust quickly to changing priorities and conditions and cope effectively with complexity and change.
A genuine curiosity about AI and how it can be applied in your area of work — from accelerating analysis and insight generation, to improving how we design and deliver processes and communications.
Practical experience using AI tools in a professional context — whether to connect information, draft content, build frameworks, or create insights from data. We want people who actively use AI to increase productivity, not those who are waiting to be told to.
Strong AI literacy — an understanding of what AI assistants can and cannot do, how to prompt effectively, and how to critically evaluate AI-generated output before using it.
Awareness of the ethical considerations and responsible use of AI in the workplace, particularly in the context of sensitive data, customer experience, and compliance.

Nice To Haves

exposure to AI models and their applications within marketing.
Prior experience using Claude (Anthropic) is a nice-to-have but not essential — what matters most is that you are genuinely engaged with AI as a tool, comfortable experimenting, and eager to share what works with your wider team

Responsibilities

Development of analytics to evaluate marketing effectiveness and ROI, using MMM, attribution modeling, CLV and experimentation.
Apply causal inference techniques (e.g., difference-in-differences, propensity score matching, uplift modeling) to isolate true marketing impact and support strategic planning.
Automate the training and deployment of updated models, ensuring the output is tested, automated, scalable and documented and checks are in place to identify drift.
Creating ‘insight-ready’ datasets from raw data by building pipelines to automate the cleaning process.
Building and supporting self-serve analytics dashboards.
Supporting the marketing team with campaign analysis, data requirements, training and ad-hoc analysis.
Communicate complex analytical findings to executive stakeholders in a clear and actionable way.