Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

About the Role

As Atmosphere scales its advertising business and venue network, we are investing in causal-inference and predictive-modeling to enhance both aspects. This role is high-impact and high-visibility, sitting at the intersection of data science, sales, product, and go-to-market. The ideal candidate is an applied statistician and modeler who understands causal inference and predictive methods, and can translate rigor into business-usable products. You will design, build, and ship models that quantify campaign impact, assess venue value and risk, and establish methodology as a competitive advantage.

Responsibilities

Design and own Atmosphere's causal measurement framework, isolating incremental impact from confounders.
Build statistically rigorous causal designs, including exposed/control groups, geo-based experiments, difference-in-differences, and synthetic control.
Establish an incrementality capability to differentiate sales offerings and build client trust.
Build models to predict venue revenue potential for acquisition prioritization and flagging under-monetized venues.
Leverage venue data to understand drivers of retention vs. churn and prioritize customer challenges.
Model and infer latent venue attributes to refine advertiser targeting segments and improve revenue/churn models.
Use measurement outputs to generate actionable insights for campaign strategy, including targeting, optimization, and segmentation.
Develop closed-loop optimization frameworks to enhance campaign performance, building proprietary benchmarks.

Requirements

5+ years of experience in data science, applied research, or quantitative analytics.
Deep fluency in causal inference and applied statistics, including experience with A/B testing, geo experiments, difference-in-differences, synthetic control, propensity methods, and regression modeling.
Expertise in predictive modeling (e.g., gradient-boosted trees, survival/churn models, calibration, and honest out-of-sample evaluation).
Sound judgment regarding model trustworthiness, including validation, uncertainty, and readiness for action.
Strong programming skills in Python and/or R.
Comfort with SQL for working with data at scale.
Proven ability to translate complex statistical work into clear, business-friendly outputs.
Demonstrated track record of shipping models and products that are utilized.
Ability to operate in a fast-moving environment and balance rigor with pragmatism.

Skills

Causal inference
Predictive modeling
A/B testing
Geo experiments
Difference-in-differences
Synthetic control
Propensity methods
Regression modeling
Gradient-boosted trees
Survival/churn models
Calibration
Out-of-sample evaluation
Python
R
SQL
Location/mobility data
Behavioral signal data
OOH advertising
DOOH advertising
CTV advertising
Ad tech
Media platforms
Measurement vendors
Marketing measurement
Media effectiveness
Mixed media modeling (MMM)
Multi-touch attribution (MTA)
Bayesian inference
Probabilistic modeling
Data product launches
Computer vision
NLP

Experience Level

Senior

Salary/Compensations

Competitive salary

Benefits

Company Equity
Company 401(k) with employer matching
Competitive insurance plans
Flexible Time Off Policy

About the Company

Atmosphere is scaling its advertising business and venue network.

Equal Opportunity

At Atmosphere, we’re committed to building a diverse, inclusive team where creativity, innovation, and teamwork thrive. If you're excited about this role but your experience doesn’t perfectly align with every qualification, we still encourage you to apply—you might be the right fit for this or another role.