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Data Scientist Lead (Contract to FTE)
HyphaMetrics
Remote Solelytechnical leadPosted March 28, 2026
About the Role
The role involves designing and analyzing experimental tests to measure advertising incrementality and building causal and marketing mix models for multi-channel attribution. Key tasks include developing and productionizing scalable data pipelines for event-level data ingestion and ensuring measurement datasets are clean and privacy-compliant.
Requirements
Candidates must have over 3 years of experience building statistical or ML models in relevant fields like ad-tech or marketing analytics, possessing strong fundamentals in statistics and causal inference. Proficiency in Python (pandas, scikit-learn) and SQL is required, along with hands-on experience with cloud data warehouses and event-level advertising data.
Full Job Description
About Hypha Metrics
Hypha Metrics is redefining how media data is measured, connected, and understood. Our platform provides the foundational infrastructure that powers audience insights for the modern media ecosystem. We help clients make smarter decisions by transforming fragmented data into unified, actionable intelligence.
Role summary
Design and deliver rigorous, scalable measurement solutions that uncover true media impact and drive media investment decisions. This role combines causal inference, experimentation, statistical modeling, and production data engineering to measure incrementality, optimize media mix and inform campaign strategy across digital and offline channels.
Key responsibilities
Design and analyze randomized and quasi-experimental tests (holdouts, geo-tests, RCTs) to measure advertising incrementality and lift.
Build and maintain causal models (difference-in-differences, synthetic controls, hierarchical Bayesian, uplift modeling) and marketing mix models (MMM) for multi-channel attribution.
Develop and productionize scalable end-to-end pipelines for event-level ad exposure, conversions, and offline-sales ingestion (ETL/ELT, validation, monitoring).
Work with data engineering to keep measurement datasets clean, deduplicated (identity resolution), and privacy-compliant.
Own feature engineering, model training, validation, and deployment in Python/R and cloud environments (BigQuery, Snowflake, Dataproc/AWS/GCP).
Produce clear, actionable dashboards and executive-ready insights for product, media, and client teams; present findings to stakeholders.
Implement automated reporting and CI/CD for model retraining and performance monitoring; establish measurement governance and documentation.
Stay current on the ad-tech/measurement ecosystem (ATtribution frameworks, walled gardens, ID solutions) and recommend measurement strategy changes.
Required qualifications
3+ years experience building statistical or ML models in ad-tech, marketing analytics, agency measurement, or a related data science role.
Strong statistics/causal inference fundamentals: experimental design, hypothesis testing, regression, hierarchical models.
Proficient in Python (pandas, scikit-learn, PyMC/Stan or equivalent) and SQL; experience with R is a plus.
Hands-on experience with event-level ad/exposure and conversion data, logs from ad servers/DSPs, or retail/point-of-sale integration.
Experience working with cloud data warehouses (BigQuery, Snowflake, Redshift) and ETL tooling (Airflow, dbt, Kafka).
Excellent written and verbal communication; proven ability to translate technical results to non-technical stakeholders.
Preferred qualifications
Experience with incrementality platforms or approaches (e.g., Measured, experimentation platforms, proprietary lift frameworks).
Familiarity with marketing mix modeling (time-series, regularized regression, Bayesian MMM).
Experience with causal ML / uplift modeling and Bayesian inference tools (PyMC3/4, Stan).
Knowledge of identity resolution, privacy-preserving measurement (privacy regulation awareness, cohort-based measurement, differential privacy concepts).
Experience deploying models to production (Docker, CI/CD, MLOps patterns) and instrumenting model monitoring.
Background working with brand/performance media, cross-channel measurement, and agency/client workflows.
KPIs / success metrics
Number of incrementality tests designed, executed, and turned into action.
Accuracy and explainability of models (e.g., holdout prediction error, calibration).
Time-to-insight (from data ingestion to stakeholder-ready report).
Business impact (measured media savings or ROI improvements attributable to recommendations).
Adoption rate of measurement outputs by media planners/clients.
Nice-to-have (company fit & soft skills)
Proven consultative experience with clients or internal stakeholders.
Comfort operating in ambiguous environments and balancing speed vs. statistical rigor.
Ability to mentor junior data scientists and evangelize measurement best practices across teams.
Hypha Metrics is redefining how media data is measured, connected, and understood. Our platform provides the foundational infrastructure that powers audience insights for the modern media ecosystem. We help clients make smarter decisions by transforming fragmented data into unified, actionable intelligence.
Role summary
Design and deliver rigorous, scalable measurement solutions that uncover true media impact and drive media investment decisions. This role combines causal inference, experimentation, statistical modeling, and production data engineering to measure incrementality, optimize media mix and inform campaign strategy across digital and offline channels.
Key responsibilities
Design and analyze randomized and quasi-experimental tests (holdouts, geo-tests, RCTs) to measure advertising incrementality and lift.
Build and maintain causal models (difference-in-differences, synthetic controls, hierarchical Bayesian, uplift modeling) and marketing mix models (MMM) for multi-channel attribution.
Develop and productionize scalable end-to-end pipelines for event-level ad exposure, conversions, and offline-sales ingestion (ETL/ELT, validation, monitoring).
Work with data engineering to keep measurement datasets clean, deduplicated (identity resolution), and privacy-compliant.
Own feature engineering, model training, validation, and deployment in Python/R and cloud environments (BigQuery, Snowflake, Dataproc/AWS/GCP).
Produce clear, actionable dashboards and executive-ready insights for product, media, and client teams; present findings to stakeholders.
Implement automated reporting and CI/CD for model retraining and performance monitoring; establish measurement governance and documentation.
Stay current on the ad-tech/measurement ecosystem (ATtribution frameworks, walled gardens, ID solutions) and recommend measurement strategy changes.
Required qualifications
3+ years experience building statistical or ML models in ad-tech, marketing analytics, agency measurement, or a related data science role.
Strong statistics/causal inference fundamentals: experimental design, hypothesis testing, regression, hierarchical models.
Proficient in Python (pandas, scikit-learn, PyMC/Stan or equivalent) and SQL; experience with R is a plus.
Hands-on experience with event-level ad/exposure and conversion data, logs from ad servers/DSPs, or retail/point-of-sale integration.
Experience working with cloud data warehouses (BigQuery, Snowflake, Redshift) and ETL tooling (Airflow, dbt, Kafka).
Excellent written and verbal communication; proven ability to translate technical results to non-technical stakeholders.
Preferred qualifications
Experience with incrementality platforms or approaches (e.g., Measured, experimentation platforms, proprietary lift frameworks).
Familiarity with marketing mix modeling (time-series, regularized regression, Bayesian MMM).
Experience with causal ML / uplift modeling and Bayesian inference tools (PyMC3/4, Stan).
Knowledge of identity resolution, privacy-preserving measurement (privacy regulation awareness, cohort-based measurement, differential privacy concepts).
Experience deploying models to production (Docker, CI/CD, MLOps patterns) and instrumenting model monitoring.
Background working with brand/performance media, cross-channel measurement, and agency/client workflows.
KPIs / success metrics
Number of incrementality tests designed, executed, and turned into action.
Accuracy and explainability of models (e.g., holdout prediction error, calibration).
Time-to-insight (from data ingestion to stakeholder-ready report).
Business impact (measured media savings or ROI improvements attributable to recommendations).
Adoption rate of measurement outputs by media planners/clients.
Nice-to-have (company fit & soft skills)
Proven consultative experience with clients or internal stakeholders.
Comfort operating in ambiguous environments and balancing speed vs. statistical rigor.
Ability to mentor junior data scientists and evangelize measurement best practices across teams.