InfluencyIQ
Data Scientist
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Data Scientist - Marketing Measurement & Causal Inference
Contract / Consultancy | Remote | Immediate start
InfluencyIQ is building a campaign intelligence platform designed to help brands understand what their marketing is actually moving across channels.
Rather than simply reporting platform attribution, InfluencyIQ connects marketing activity with search, web, commerce, CRM, social and other business signals to understand how demand is generated, progresses through the customer journey and is ultimately captured.
We are looking for an experienced Data Scientist to review, validate and formalise the statistical methodology behind our Intelligence Engine.
This is initially a focused consultancy project rather than a broad data science role.
The Project
We have already developed the product and analytical framework for the Intelligence Engine, including:
- campaign baselines and expected performance
- event-response analysis
- lagged relationships
- contributor-to-outcome analysis
- cross-signal pathways
- overlapping marketing activity
- confounder detection and treatment
- repeated relationships across campaigns and entities
- contributor comparison
- evidence classification
- multiple-testing / false-positive control
- progressive intelligence where customer history is limited
- structured analytical outputs for downstream AI interpretation
Your role will be to take this product framework and determine the most statistically defensible way to implement it.
You will not be starting from a blank page. We have a detailed Measurement & Analysis Framework which defines what the product needs to achieve, the analytical principles it must follow, and where specialist statistical decisions are still required.
What We Need You to Do
You will review the framework and:
- validate or challenge the proposed analytical methodology
- define appropriate statistical methods for different data and relationship types
- formalise baseline methodologies and minimum data requirements
- define approaches to trend, seasonality and autocorrelation
- establish event-response and lag-analysis methodology
- determine when Pearson, Spearman, regression, count models, time-series methods or other approaches are appropriate
- recommend appropriate quasi-experimental methods where natural controls exist
- define treatment of overlapping contributors and multicollinearity
- establish methodology for repeated activations and entity-level learning
- formalise multiple-testing and false-discovery controls
- define effect-size, uncertainty and statistical-reliability requirements
- establish criteria for evidence classifications such as Strong, Moderate and Limited
- define when the system should return No Detectable Relationship, Insufficient Evidence or Unable to Isolate
- formalise how pathway-supported contribution should be evaluated
- define rules for progressive analytical maturity as additional evidence accumulates
- help design a synthetic validation and backtesting framework
- document the resulting methodology clearly enough for our engineering team to implement in production
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You may also be asked to prototype or provide reference implementations of selected analytical methods.
A Key Measurement Problem We Are Solving
A core part of InfluencyIQ is distinguishing attribution from contribution.
For example, a customer may:
- see a product through a creator campaign
- subsequently search for the product
- click a paid Google result
- purchase
Google Ads may receive direct conversion attribution because it captured the final click. InfluencyIQ needs to analyse the wider evidence to determine whether creator activity appears to have generated the upstream demand subsequently captured by paid search, without making unsupported causal or monetary attribution claims.
We are looking for someone who is comfortable solving this type of problem using incomplete, multi-source observational data.
Essential Experience
You should have strong practical experience in several of the following:
- statistical modelling
- causal inference
- time-series analysis
- experimental or quasi-experimental design
- event studies
- regression modelling
- treatment of confounding and selection bias
- multiple-hypothesis testing
- uncertainty estimation
- repeated-measures or hierarchical modelling
- observational data analysis
- model validation and backtesting
You should also be comfortable translating statistical methodology into clear decision rules that software engineers can implement.
Strong Python experience and familiarity with mainstream statistical/data science libraries is expected.
Particularly Relevant Backgrounds
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Experience in marketing analytics is useful but not essential.
We would be particularly interested in candidates with backgrounds in:
- marketing measurement
- econometrics
- causal inference
- experimentation
- product analytics
- marketplace analytics
- advertising measurement
- media effectiveness
- marketing mix modelling
- attribution
- commercial analytics
Candidates from other sectors are equally welcome where the underlying methodological experience is strong.
Nice to Have
Experience with any of the following would be useful:
- Marketing Mix Modelling
- Multi-Touch Attribution
- incrementality testing
- geo experiments
- difference-in-differences
- synthetic controls
- Bayesian methods
- adstock and saturation modelling
- digital advertising platforms
- GA4 / Google Ads / Meta Ads
- Shopify or ecommerce data
- Google Search Console
- CRM data
- creator / influencer marketing measurement
What We Are Not Looking For
This is not primarily:
- a dashboard / BI role
- a data engineering role
- an LLM engineering role
- a generic machine-learning role
- a paid-media optimisation role
We specifically need someone with depth in measurement, statistical inference and observational-data methodology.
Initial Deliverables
The first phase is expected to produce:
- A reviewed and annotated version of our Measurement & Analysis Framework.
- A formal statistical methodology specification.
- Recommended models, thresholds, diagnostics and decision rules.
- Clear identification of any areas where the proposed methodology should change.
- A validation and synthetic-testing plan.
- A handover suitable for our engineering team to productionise.
There may be an opportunity for an ongoing advisory relationship as InfluencyIQ develops more advanced modelling, including incrementality, longitudinal organisation intelligence and future cross-organisation recommendation and budget-allocation models.
About InfluencyIQ
InfluencyIQ is a campaign intelligence platform designed to connect marketing activity with business outcomes across channels.
Our aim is to move beyond siloed platform reporting and simplistic last-click attribution, giving marketing teams a clearer view of what appears to generate demand, what captures it, how signals move across the customer journey and what they should do next.
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