Deep grounding in causal inference methodologies, including causal forests, treatment-effect ... CRM, financial systems, or telemetry data) in partnership with data engineering teams. • ...
Deep grounding in causal inference methodologies, including causal forests, treatment-effect ... CRM, financial systems, or telemetry data) in partnership with data engineering teams. • ...
Manager, Product and Marketing Analytics
Chicago, IL · On-site
$150K - $170K/yr
In this role, you'll define our company-wide measurement strategy, lead causal inference and ... Ability to manage multiple high-stakes initiatives simultaneously; thrive in a fast-paced startup.
Manager, Product and Marketing Analytics
Chicago, IL · On-site
$150K - $170K/yr
In this role, you'll define our company-wide measurement strategy, lead causal inference and ... Ability to manage multiple high-stakes initiatives simultaneously; thrive in a fast-paced startup.
Deep grounding in causal inference methodologies, including causal forests, treatment-effect ... as CRM, financial systems, or telemetry data) in partnership with data engineering teams.
Deep grounding in causal inference methodologies, including causal forests, treatment-effect ... as CRM, financial systems, or telemetry data) in partnership with data engineering teams.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
... causal inference approaches; ensure test validity and clear readouts. • Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners. • ...
... causal inference approaches; ensure test validity and clear readouts. • Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners. • ...
Apply statistical and machine learning techniques such as causal inference, recommender systems, and time series forecasting to generate insights or deploy models. * Manage your own project end-to ...
Apply statistical and machine learning techniques such as causal inference, recommender systems, and time series forecasting to generate insights or deploy models. * Manage your own project end-to ...
... causal inference approaches; ensure test validity and clear readouts. • Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners. • ...
... causal inference approaches; ensure test validity and clear readouts. • Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners. • ...
... causal inference approaches; ensure test validity and clear readouts. • Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners. • ...
... causal inference approaches; ensure test validity and clear readouts. • Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners. • ...
Lead Decision Science Analyst
Oak Brook, IL · On-site
$102K - $193K/yr
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
Lead Decision Science Analyst
Oak Brook, IL · On-site
$102K - $193K/yr
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
Lead Decision Science Analyst
Oak Brook, IL · Hybrid
$102K - $193K/yr
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
Lead Decision Science Analyst
Oak Brook, IL · Hybrid
$102K - $193K/yr
... other causal inference approaches; ensure test validity and clear readouts. * Develop, measure, and monitor product or customer KPIs in collaboration with product managers and finance partners.
Research Professional - Eyal Frank (Full-Time, Benefits Eligible)
Chicago, IL · On-site
$55K - $62K/yr
The project will use causal inference methods, as well as recent advances in machine learning ... The Research Professional's responsibilities span all stages of research, including managing ...
New
Research Professional - Eyal Frank (Full-Time, Benefits Eligible)
Chicago, IL · On-site
$55K - $62K/yr
The project will use causal inference methods, as well as recent advances in machine learning ... The Research Professional's responsibilities span all stages of research, including managing ...
New
Manager Causal Inference information
See Chicago, IL salary details
$29.9K - $38.2K
1% of jobs
$38.2K - $46.5K
5% of jobs
$46.5K - $54.9K
3% of jobs
$54.9K - $63.2K
3% of jobs
$63.2K - $71.5K
3% of jobs
$71.5K - $79.9K
1% of jobs
$79.9K - $88.2K
1% of jobs
$88.2K - $96.6K
1% of jobs
$96.6K - $104.9K
1% of jobs
$104.9K - $113.2K
1% of jobs
$113.6K is the 25th percentile. Wages below this are outliers.
$113.2K - $121.6K
79% of jobs
$29.9K
$107.7K
$121.6K
How much do manager causal inference jobs pay per year?
As of Jul 18, 2026, the average yearly pay for manager causal inference in Chicago, IL is $107,728.00, according to ZipRecruiter salary data. Most workers in this role earn between $117,400.00 and $120,000.00 per year, depending on experience, location, and employer.
How does a Manager of Causal Inference typically collaborate with cross-functional teams to drive impactful business insights?
Managers of Causal Inference frequently work alongside data scientists, product managers, engineers, and business leaders to design and execute experiments that reveal the true impact of business decisions. They translate complex statistical findings into actionable recommendations, ensuring stakeholders understand both the methodology and implications. Regularly, they lead discussions on experiment design, data collection strategies, and result interpretation, fostering a culture of evidence-based decision-making across the organization.
What does a Manager Causal Inference do?
A Manager Causal Inference leads teams that analyze data to determine cause-and-effect relationships, often in business, healthcare, or technology settings. They design experiments or use statistical methods to understand how different factors influence outcomes, helping organizations make data-driven decisions. This role typically involves managing projects, overseeing analysts or data scientists, and communicating findings to stakeholders. Strong expertise in statistics, data analysis, and leadership is essential for success in this position.
What are the key skills and qualifications needed to thrive as a Manager of Causal Inference, and why are they important?
To thrive as a Manager of Causal Inference, you need a deep understanding of statistics, econometrics, and experimental design, typically supported by an advanced degree in a quantitative field. Proficiency with data analysis tools such as R, Python, SQL, and specialized causal inference libraries, along with experience using data visualization and project management platforms, is crucial. Strong leadership, communication, and critical thinking skills help you effectively guide teams and translate complex findings to stakeholders. These skills ensure rigorous, actionable insights that drive strategic decision-making and organizational impact.
What are the most commonly searched types of Causal Inference jobs in Chicago, IL? The most popular types of Causal Inference jobs in Chicago, IL are:
What are popular job titles related to Manager Causal Inference jobs in Chicago, IL? For Manager Causal Inference jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Manager Causal Inference jobs in Chicago, IL look for? The top searched job categories for Manager Causal Inference jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Manager Causal Inference jobs? Cities near Chicago, IL with the most Manager Causal Inference job openings:
Full-time
Posted 21 hours ago
Job description
Job Summary:
Everpure is reshaping the data storage industry and is seeking a Principal Economist to drive go-to-market efficiency through advanced statistical methodologies. This role involves leading causal measurement science efforts, collaborating with various executives, and transforming large datasets into actionable insights.
Responsibilities:
• Own the Go-To-Market Causal Measurement Strategy: Architect and execute the long-term roadmap for measuring sales incrementality, partnering with senior Sales, Finance, and Operations executives to deliver highly accurate, actionable insights that guide global investment decisions.
• Design and Deploy Advanced Causal Models: Build, validate, and scale state-of-the-art observational models (such as causal forests, panel methods, and uplift models) in R or Python to isolate true headcount-driven lift and incentive plan performance from external market demand and noise.
• Drive Investment ROI and Strategic Allocation: Quantify the incremental impact of sales headcount, incentive modifications, and customer-facing programs (e.g., proofs of concept, workshops) for executive leaders (CFO, CRO, CMO) to directly influence headcount planning, NRR growth, and resource allocation.
• Operationalize Production-Grade Data Pipelines: Partner with Data Engineering, Analytics, and GTM Operations to establish robust, stable, and maintainable pipelines and data infrastructure, translating complex econometrics into automated dashboards and reusable frameworks for continuous business planning.
Qualifications:
Required:
• Advanced Econometric & Causal Inference Expertise: Deep grounding in causal inference methodologies, including causal forests, treatment-effect heterogeneity, synthetic control, and difference-in-differences analyses.
• Statistical Computing Fluency: Advanced proficiency in R, Python, or comparable statistical computing environments (such as causalTree, EconML, or statsmodels) to write clean, repeatable, and production-ready code.
• Data Engineering & Production Deployment Experience: Demonstrated capability to architect, deploy, and scale production-grade causal analyses utilizing messy, complex business datasets (such as CRM, financial systems, or telemetry data) in partnership with data engineering teams.
• Executive-Level Communication & Stakeholder Influence: Exceptional capacity to translate complex econometric and statistical results into clear narratives and strategic recommendations for non-technical corporate leaders.
Preferred:
• PhD in Economics, Econometrics, or an equivalent quantitative discipline, such as Applied Economics or Quantitative Marketing
Company:
We are Everpure. We don’t just store data—we bring it to life. Founded in , the company is headquartered in , , with a team of 5001-10000 employees. The company is currently Late Stage.
Everpure is reshaping the data storage industry and is seeking a Principal Economist to drive go-to-market efficiency through advanced statistical methodologies. This role involves leading causal measurement science efforts, collaborating with various executives, and transforming large datasets into actionable insights.
Responsibilities:
• Own the Go-To-Market Causal Measurement Strategy: Architect and execute the long-term roadmap for measuring sales incrementality, partnering with senior Sales, Finance, and Operations executives to deliver highly accurate, actionable insights that guide global investment decisions.
• Design and Deploy Advanced Causal Models: Build, validate, and scale state-of-the-art observational models (such as causal forests, panel methods, and uplift models) in R or Python to isolate true headcount-driven lift and incentive plan performance from external market demand and noise.
• Drive Investment ROI and Strategic Allocation: Quantify the incremental impact of sales headcount, incentive modifications, and customer-facing programs (e.g., proofs of concept, workshops) for executive leaders (CFO, CRO, CMO) to directly influence headcount planning, NRR growth, and resource allocation.
• Operationalize Production-Grade Data Pipelines: Partner with Data Engineering, Analytics, and GTM Operations to establish robust, stable, and maintainable pipelines and data infrastructure, translating complex econometrics into automated dashboards and reusable frameworks for continuous business planning.
Qualifications:
Required:
• Advanced Econometric & Causal Inference Expertise: Deep grounding in causal inference methodologies, including causal forests, treatment-effect heterogeneity, synthetic control, and difference-in-differences analyses.
• Statistical Computing Fluency: Advanced proficiency in R, Python, or comparable statistical computing environments (such as causalTree, EconML, or statsmodels) to write clean, repeatable, and production-ready code.
• Data Engineering & Production Deployment Experience: Demonstrated capability to architect, deploy, and scale production-grade causal analyses utilizing messy, complex business datasets (such as CRM, financial systems, or telemetry data) in partnership with data engineering teams.
• Executive-Level Communication & Stakeholder Influence: Exceptional capacity to translate complex econometric and statistical results into clear narratives and strategic recommendations for non-technical corporate leaders.
Preferred:
• PhD in Economics, Econometrics, or an equivalent quantitative discipline, such as Applied Economics or Quantitative Marketing
Company:
We are Everpure. We don’t just store data—we bring it to life. Founded in , the company is headquartered in , , with a team of 5001-10000 employees. The company is currently Late Stage.