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About 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 where you will own the methodology and client relationship, lead a multi-workstream data science team, and stay close enough to the models to challenge assumptions, review code, and unblock technical problems. You will also help productize measurement work into reusable accelerators and points of view.

Responsibilities

Own the hardest question in marketing: what actually caused the sales.
Lead measurement programs, owning the design, execution, and interpretation of MMM, geo and incrementality testing, attribution, experimentation, and marketing optimization.
Own the statistical modeling architecture and scientific rigor, including model specification, validation, calibration, uncertainty treatment, and assumption identification.
Adapt frameworks to messy commercial reality, including long consideration cycles, CRM-driven demand, short flights, campaign overlap, brand and long-term effects, channel and regional constraints, and privacy-constrained measurement.
Translate model output into actionable media investment, budget allocation, and scenario-planning decisions.
Own the outcome, not just the deliverable, working in the client's environment from day zero and staying after launch.
Build and deploy GenAI and agentic applications into production to change how measurement is done and consumed.
Contribute to Artefact's reusable measurement offerings, accelerators, and IP.
Get direct access to frontier labs and build with partners like Anthropic, Google, OpenAI, and Mistral.
Build and lead data scientists and analysts across parallel workstreams, growing senior talent.
Set delivery standards for reproducibility, documentation, QA, and validation.
Work alongside scientists, engineers, creatives, and strategists.
Shape solutions, support proposals, and scope new work to expand client relationships.
Represent Artefact externally through white papers, conference talks, and published methodologies.
Help decide what to productize next, stop doing, and how to take it to market.
Get direct access to senior client stakeholders, presenting methods, defending them, owning recommendations, and participating in qualification.

Requirements

Approximately 10+ years building Marketing Mix Modeling, incrementality, attribution work, applied statistics, causal inference, experimentation, and marketing optimization, or equivalent depth.
Shipped models that changed how a real budget was spent, and ability to discuss failed models.
Genuine marketing fluency, understanding of retail media, paid social, brand-building, and media planner actions.
Proficiency in causal inference and Bayesian methods as a working craft.
Comfortable with forecasting, optimization, experimentation platforms, and applied GenAI.
Experience shipping generative AI or agentic AI applications into production for marketing, analytics, and decision-making.
Still writing and debugging code personally.
Strong client-facing and executive communication skills, making methodology legible to executives and business stakeholders.
Ability to hold a technical audience for an hour and a senior client stakeholder for ninety seconds.
Ability to state when a result is inconclusive.
Ability to lead complex and ambiguous measurement programs while remaining hands-on.
Experience leading and developing data-science teams across multiple workstreams.
Consulting and commercial mindset, shaping solutions, supporting proposals, and expanding client relationships.
Experience being in the room during qualification and solution design.
Production instincts, understanding MLOps and cloud industrialization.
AI-native ways of working, using AI daily.
Master's degree (or higher) in computer science, engineering, statistics/mathematics, or a related field, or equivalent research/industry experience.
Financial-services experience (banking, wealth management, asset management, credit cards, or insurance) is preferred but can be traded for exceptional measurement and AI strength.
Consulting, agency, measurement-vendor, or platform background in data-intensive industries is preferred.
Experience with brand and long-term marketing measurement is preferred.
Experience with major media platforms and ecosystems and their measurement products is preferred.
Evidence of external thought leadership (white papers, published methodologies, conference participation, reusable offerings) is preferred.
Experience with visualization and delivery tooling (Looker, Tableau, Power BI) and cloud data platforms (BigQuery, Snowflake, Databricks) is preferred.

Skills

Marketing Mix Modeling
Incrementality testing
Attribution
Experimentation
Marketing optimization
Statistical modeling
Causal inference
Bayesian methods
Forecasting
Optimization
Experimentation platforms
Applied GenAI
Generative AI
Agentic AI
MLOps
Cloud industrialization
Client-facing communication
Executive communication
Team leadership
Consulting
Commercial mindset
Retail media
Paid social
Brand building
Media planning
Looker
Tableau
Power BI
BigQuery
Snowflake
Databricks

Location

Work Type

Hybrid

Experience Level

Director
10+ years

Education Level

Master's degree or higher in computer science, engineering, statistics/mathematics, or related field, or equivalent research/industry experience

Salary/Compensations

$200,000 to $230,000

Benefits

Medical, dental, and vision coverage
401(k) plan with company matching
Paid parental leave
Unlimited paid time off
Learning and Development opportunities
Hybrid Flexibility

About the Company

Artefact is an applied AI company that builds systems that run in production.
We have 2,500+ people across 27 countries and 1,000 clients, including major global consumer brands.
We combine data at enterprise scale, an applied AI team for production environments, and transformation muscle for user adoption.
Agentic AI is transforming marketing, changing how consumers browse, compare, and buy, and challenging traditional visibility and performance measurement.