Measurement Strategist

Darkroom•New York, NY

4d•Remote

About The Position

Darkroom is hiring a Measurement Strategist to own the measurement program for a dedicated client engagement. You’ll build the data foundation, design and execute experiments, manage external measurement partners, and translate findings into decisions about media investment and growth. This is a highly technical, hands-on role for someone who can work directly with complex datasets, design rigorous incrementality tests, and explain what the results mean for the business. You’ll work closely with Darkroom’s growth, media, and creative teams, alongside client stakeholders across product, engineering, data, and finance. Your work will connect marketing investment to acquisition, activation, revenue, retention, and sustainable customer economics. As the media mix expands across channels and markets, you’ll help determine what is driving incremental growth and where the next dollar should go. This position is fully allocated to one client.

Requirements

Ideally 5–6+ years of relevant experience in marketing science, measurement, growth analytics, or a closely related field, with direct ownership of analytical work and experimentation.
Advanced SQL and strong Python or R skills. You can independently extract, join, clean, validate, and analyze large datasets, and automate recurring work.
Demonstrated incrementality expertise, including hands-on experience designing and analyzing controlled experiments, applying power analysis, and interpreting uncertain or inconclusive results.
Practical MMM experience, including preparing data, working with modeling partners, evaluating methodology, and translating findings into investment decisions.
Experience establishing measurement infrastructure across advertising platforms, web or app analytics, customer data, and revenue systems.
Strong understanding of full-funnel media, attribution limitations, cohort analysis, CAC, LTV, payback, and contribution margin.
Experience with cloud data warehouses and visualization tools, with the ability to build reliable analytical models and reporting.
Clear communication and strong ownership: you can work independently, collaborate with technical teams, and defend a recommendation with evidence.

Nice To Haves

Measurement programs built from launch or during rapid growth.
Brand and performance measurement across multiple markets, including channels with limited user-level attribution.
Subscription, recurring revenue, or other businesses where retention and customer lifetime value guide acquisition decisions.
Turning forecasts and scenario models into tools teams use for ongoing investment decisions.

Responsibilities

Data architecture and measurement infrastructure: Audit the existing measurement setup and establish the architecture connecting media delivery, web and app activity, customer behavior, and revenue. Define event taxonomies, campaign naming conventions, KPI definitions, attribution rules, and reporting requirements so teams work from consistent, reliable data. Build and maintain analytical datasets, SQL queries, automated workflows, and dashboards. Reconcile discrepancies across platforms and establish ongoing data quality checks. Partner with engineering and implementation teams on tracking, integrations, identity matching, and data pipelines, owning measurement requirements and validating implementation.
Incrementality and experimentation: Own a prioritized testing roadmap tied to business growth questions, including channel incrementality, audience and geographic expansion, creative effectiveness, and downstream customer quality. Design and execute geo holdouts, matched-market tests, audience holdouts, platform conversion and brand lift studies, and controlled A/B experiments. Establish hypotheses, primary outcomes, power analyses, minimum detectable effects, budgets, test duration, and decision criteria before launch. Coordinate execution with media and client teams, monitor test integrity, and deliver readouts that clearly explain incremental impact, uncertainty, business implications, and recommended next steps.
MMM and measurement partner management: Own day-to-day relationships with external marketing mix modeling and incrementality partners, including onboarding, data delivery, methodology reviews, and recurring analyses. Assess whether available history, spend variation, and outcome volume support reliable modeling; sequence the measurement roadmap accordingly. Pressure-test model assumptions and outputs, including baseline demand, lagged effects, saturation, and the influence of product or market changes. Bring together experiment results, MMM, attribution, and business performance to inform channel response curves, marginal returns, and budget scenarios.
Measurement across the media mix and customer journey: Build a KPI framework connecting awareness and acquisition to activation, conversion, retention, and customer value. Develop measurement approaches for a diversified mix spanning paid social, search, online video, creators, programmatic, CTV, audio, and offline channels as the program expands. Evaluate how demand creation influences search, direct traffic, and subsequent conversion, accounting for the different roles channels play. Analyze cohorts by acquisition source, market, creative, audience, and customer segment to identify which investments generate durable business value.
Growth analysis and decision support: Maintain and refine growth forecasts and investment scenarios using observed performance, experiment findings, cohort economics, and partner modeling outputs. Identify diminishing returns, opportunities to expand investment, and constraints in activation, conversion, retention, or profitability. Build clear reporting and lead measurement discussions with internal and client stakeholders, translating complex findings into specific recommendations on what to scale, change, or test next.