Staff Data Scientist, Growth and Marketing

TechChain Talent•New York, NY

11d•Onsite

About The Position

The team is seeking an experienced, versatile Data Scientist who thrives in a fast paced environment. You will have the freedom to identify opportunities, design and implement solutions, and measure their impact across the product, collaborating with cross functional teams while owning your projects from conception to completion. Own the measurement and analytics framework for a growing marketing and paid acquisition effort, helping determine where and how significant marketing budgets are deployed. Develop frameworks for incrementality, attribution, CAC, LTV, ROAS, payback periods and channel efficiency, optimising for real business impact rather than platform reported performance. Design and analyse experiments across creative, audience, channel, bidding and landing page strategies to identify what drives incremental acquisition, engagement and revenue. Build models and analytical tooling to understand user quality and predict downstream value across campaigns, channels, cohorts and acquisition sources. Partner with Growth and Marketing to turn complex data into clear recommendations on budget allocation, campaign strategy and where to scale or reduce spend. Combine advertising platform data with first party product, behavioural and on chain data to build a complete view of the acquisition funnel and customer lifecycle. Build foundational data models, dashboards and automated reporting so teams can understand marketing performance in real time. Improve marketing data infrastructure, tracking and eventing with Engineering, ensuring reliable data from impression through to downstream product behaviour. Contribute to the wider Data Science function across product performance, experimentation, user behaviour, ecosystem analysis and competitive intelligence.

Requirements

An experienced Data Scientist with significant exposure to paid acquisition, performance marketing or growth analytics within a scaled consumer technology business.
Highly proficient in Python or R and SQL, working independently across large and complex datasets.
Deeply familiar with measuring paid marketing performance: attribution, incrementality, selection bias and platform reported metrics.
Experienced designing and analysing experiments, including geo tests, holdouts, A/B tests or other causal inference approaches for marketing effectiveness.
Fluent in CAC, LTV, ROAS, retention, conversion and payback, and how those metrics interact across the customer lifecycle.
Able to move beyond what happened to why it happened, what will happen next and what should be done about it.
Comfortable building data models and lightweight pipelines, ideally with dbt, Dagster and BigQuery, and experienced with visualisation and dashboarding tools for decision oriented reporting.
A high agency owner of ambiguous, high impact problems from initial question through to recommendation and implementation.
An excellent communicator who influences Marketing, Product and Engineering decisions through data, with strong product and commercial intuition.

Nice To Haves

Experience with crypto, blockchain or on chain data is beneficial but not essential.
Large scale paid acquisition budgets across Meta, Google, TikTok, X or other major platforms.
Marketing mix modelling, multi touch attribution, incrementality testing or other advanced measurement frameworks.
LTV, propensity, conversion or user quality models used to inform acquisition strategy or bidding.
Advertising platform APIs, mobile attribution platforms or customer data platforms.
High growth consumer product, marketplace, fintech, gaming or social platform experience.
Machine learning applied to growth, marketing or user behaviour.
Crypto or on chain data experience or interest.

Responsibilities

Own the measurement and analytics framework for a growing marketing and paid acquisition effort, helping determine where and how significant marketing budgets are deployed.
Develop frameworks for incrementality, attribution, CAC, LTV, ROAS, payback periods and channel efficiency, optimising for real business impact rather than platform reported performance.
Design and analyse experiments across creative, audience, channel, bidding and landing page strategies to identify what drives incremental acquisition, engagement and revenue.
Build models and analytical tooling to understand user quality and predict downstream value across campaigns, channels, cohorts and acquisition sources.
Partner with Growth and Marketing to turn complex data into clear recommendations on budget allocation, campaign strategy and where to scale or reduce spend.
Combine advertising platform data with first party product, behavioural and on chain data to build a complete view of the acquisition funnel and customer lifecycle.
Build foundational data models, dashboards and automated reporting so teams can understand marketing performance in real time.
Improve marketing data infrastructure, tracking and eventing with Engineering, ensuring reliable data from impression through to downstream product behaviour.
Contribute to the wider Data Science function across product performance, experimentation, user behaviour, ecosystem analysis and competitive intelligence.