Director, AI & Data Science (Marketing Measurement & Effectiveness)

The Role
We are hiring a Director to lead marketing measurement and effectiveness for our US client portfolio, starting with a large, in-flight measurement program for a major US financial services client.

This is a player-coach role. You will own the methodology and the client relationship, lead a multi-workstream data science team, and stay close enough to the models to challenge assumptions, review code and unblock hard technical problems yourself. You will also help us productize what we build — turning measurement work into reusable accelerators and points of view that scale across accounts.

You should be able to step into a live, complex measurement program quickly and earn credibility with senior client stakeholders in the first weeks, not the first quarter.

What You'll Do
You will own the hardest question in marketing: what actually caused the sales. Marketing Mix Modeling, incrementality testing, geo-lift, attribution — for a brand where a few points of budget reallocation is worth more than most consulting engagements. You will do it as a hands-on scientist and as a trusted advisor to senior client stakeholders — both, not either.

Lead measurement programs

Own the design, execution and interpretation of marketing measurement programs: MMM, geo and incrementality testing, attribution, experimentation and marketing optimization.
Own the statistical modeling architecture and the scientific rigor behind it — model specification, validation, calibration against live experimental results, honest treatment of uncertainty, and identification assumptions worked through with people who care as much as you do about getting them right.
Adapt frameworks to messy commercial reality: long consideration cycles, CRM-driven demand, short flights, campaign overlap, brand and long-term effects, channel and regional constraints, and privacy-constrained measurement where the easy signal has disappeared.
Translate model output into media investment, budget allocation and scenario-planning decisions the client can act on.

Own the outcome, not just the deliverable

Work in the client's environment from day zero and stay there after launch — no prototype handoffs. When something breaks in production, you're close enough to see it and fix it.
Build and deploy GenAI and agentic applications into production that change how measurement gets done and consumed: automated model diagnostics, insight generation, analyst copilots, conversational access to measurement results.
Contribute to Artefact's reusable measurement offerings, accelerators and IP.
Get direct access to the frontier labs — we work with Anthropic, Google, OpenAI and Mistral as partners, not vendors on a procurement list, with early access and the freedom to build with what shipped this quarter.

Build and lead the team

Lead and develop data scientists and analysts across parallel workstreams; grow senior talent.
Set the delivery standards — reproducibility, documentation, QA and validation of analytical deliverables.
Work alongside scientists, engineers, creatives and strategists who will pick your models apart and then help you fix them — nobody here ships alone.

Grow the account

Shape solutions, support proposals and scope new work; expand the relationship beyond the initial mandate.
Represent Artefact externally — white papers, conference talks, published methodologies.
Help decide what we productize next, what we stop doing, and how we take it to market — measurement is not a finished product.
Get direct access to senior client stakeholders, not a workstream lead reporting up through three layers: you present the method, defend it, own the recommendation, and you're in the room during qualification, before anything is signed.

What We're Looking For

Deep marketing measurement expertise. Approximately 10+ years building — not just presenting — Marketing Mix Modeling, incrementality and attribution work, applied statistics, causal inference, experimentation and marketing optimization, or the depth to match. You have shipped models that changed how a real budget was spent, and you can talk about the ones that failed.
Genuine marketing fluency. You know how retail media behaves differently from paid social, why brand-building resists short-term measurement, and what a media planner is actually going to do with your output on Monday morning. Measurement without marketing intuition produces confident nonsense.
Serious data science. Causal inference and Bayesian methods as a working craft, not vocabulary — comfortable across the wider toolkit: forecasting, optimization, experimentation platforms, and applied GenAI, with experience shipping generative AI or agentic AI applications into production for marketing, analytics and decision-making. You're still writing code yourself — you'd rather debug it than delegate it.
Executive-grade communication. Strong client-facing and executive communication skills — able to make rigorous methodology legible to executives and business stakeholders, and to defend analytical approaches with skeptical senior stakeholders. You can hold a technical audience for an hour on the identification strategy and a senior client stakeholder for ninety seconds on what to do about it, and be trusted by both — including saying "the result is inconclusive" when it is.Hands-on program leadership. The ability to lead complex and ambiguous measurement programs while remaining close enough to the work to challenge methodologies, review models and solve difficult technical problems yourself.
Team leadership. Experience leading and developing data-science teams across multiple workstreams.
A consulting and commercial mindset — shaping solutions, supporting proposals and expanding client relationships, and being in the room during qualification and solution design, not handed a signed scope.
Production instincts. MLOps and cloud industrialization — a model living in a notebook is a hobby, not a deliverable.
AI-native ways of working. You use AI on your own work daily. If you would rather keep the ways of working you already know, ours will frustrate you.
Master's degree (or higher) in computer science, engineering, statistics/mathematics, or a related field, or equivalent research/industry experience.

Preferred

Financial-services experience, ideally in banking, wealth management, asset management, credit cards or insurance — this is domain depth we do not have internally and it transfers across accounts, so it's a priority filter, but we will trade it against exceptional measurement and AI strength.
Consulting, agency, measurement-vendor or platform background across data-intensive industries such as retail, CPG, healthcare or financial services (e.g., MMM/measurement providers, media agencies, or in-house measurement functions).
Experience with brand and long-term marketing measurement (brand tracking, brand equity, long-term effects alongside short-term performance).
Experience with major media platforms and ecosystems (Google, Meta, retail media, CTV) and their measurement products.
Evidence of external thought leadership: white papers, published methodologies, conference participation, or development of reusable measurement offerings.
Visualization and delivery tooling: Looker, Tableau, Power BI or equivalent; cloud data platforms (BigQuery, Snowflake, Databricks).