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

135 W 26th Street, New York, NY 10001; Montréal, Quebec, Canada
Full time
Work at office

Salary: Negotiable
Deadline: 2 Nov 202613 days left
Vacancy: Not specified
Category: IT & Telecommunication

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Summary

Vacancy: Not specified

Requirements

Additional Requirements

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).
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.

Responsibilities

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Qualifications, experience and the skills that matter most, along with anything that would make you stand out.

Practical next steps, the documents to prepare and the dates to keep in mind before you apply or get in touch.

Contact details, links to the official source and the notes our team adds after checking each listing by hand.

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Salary & benefits

A clear overview of what is expected, written by the people who posted it, with every detail you need to decide.

Qualifications, experience and the skills that matter most, along with anything that would make you stand out.

Practical next steps, the documents to prepare and the dates to keep in mind before you apply or get in touch.

Contact details, links to the official source and the notes our team adds after checking each listing by hand.

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