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Manager Causal Inference Jobs in New York (NOW HIRING)

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Data Scientist

Manhattan, NY · On-site

Implement CI/CD principles and manage code repositories using GitHub Enterprise. Required Qualifications Strong knowledge of hypothesis testing, OLS, GLM, and causal inference techniques. Proficiency ...

Prolific

Data Science Manager

New York, NY · On-site

Set clear expectations for technical quality - rigorous causal inference, well-designed experiments ... not just manage process * Expertise in experimentation and causal inference; you know the ...

Uber

Applied Scientist II

New York, NY · On-site

... causal inference questions and designing intricate experiments. Problems we're working on include ... management skills and strong business acumen to help align learning agendas with strategic ...

CookUnity

Senior Manager, Data Science/ML

Manhattan, NY · On-site

... causal inference, generative AI) and their application to product problems • Translate complex ... managing data scientists or ML engineers • Proven track record building and deploying ML models ...

Uber

Applied Scientist II

New York, NY

... causal inference questions and designing intricate experiments.Problems we're working on include ... management skills and strong business acumen to help align learning agendas with strategic ...

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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.

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.
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Manager Causal Inference jobs near you

Senior Applied Economist, Causal Inference & Forecasting

Navan

New York, NY

$100.70K - $127.50K/yr

Other

Posted 7 days ago

Job description

Navan is seeking a Senior Applied Economist to join the Data Science & Machine Learning team. This is a foundational, "first-of-its-kind" role at Navan, designed for a technical leader who can bridge the gaps between hands-on machine learning, rigorous economic theory, and driving business outcomes.

In this role, you will be the primary architect of our internal economic "brain." You will move beyond point-estimate forecasting to build sophisticated models that account for market nuances, uncertainty, and causal drivers. You will partner closely with Finance, Treasury, and FP&A to steer the company's financial trajectory, while providing the strategic frameworks that Sales and Pricing teams use to maximize customer adoption and revenue.

What You'll Do:

  • Next-Generation Forecasting: Uplevel our existing forecasting pipelines (currently built on Prophet). You will integrate econometric rigor to improve accuracy and, crucially, provide a range of likely outcomes (probabilistic forecasting) that Finance and Treasury can rely on for risk management.
  • Causal Inference & Strategy: Design and execute experimental and quasi-experimental frameworks to identify the "levers" of the business. You will answer critical questions regarding price elasticity, product feature attribution, and the ROI of sales incentives.
  • Strategic Blueprinting: Partner with Sales and Account Management to create data-driven frameworks for pricing and customer retention. You will translate complex causal models into actionable blueprints for go-to-market teams.
  • Production-Level Data Science: Work hands-on within our ML infrastructure. You will write production-quality Python code to deploy models into our AWS and Snowflake-based ecosystem, ensuring your insights are automated and scalable.
  • Internal Advisory: Act as the subject matter expert on economic literature and methodology, translating technical findings into strategic recommendations for executive leadership.

What We're Looking For:

  • Education: An advanced degree (PhD preferred, Masters required) in Economics, Statistics, or a related quantitative field with a heavy emphasis on econometrics or causal inference.
  • Experience: 4+ years of post-academic experience in an applied research, finance, or data science role, ideally within a high-growth tech environment or fintech.
    • Technical Proficiency:
      • Deep expertise in Python and its data science ecosystem (pandas, statsmodels, scikit-learn, etc.).
      • Advanced SQL skills, with experience querying large-scale data warehouses like Snowflake.
      • Experience working in production environments and a strong understanding of the ML lifecycle is nice to have.
  • Econometric Mastery: Proven ability to apply advanced methods (e.g., Synthetic Control, IV, Diff-in-Diff, Structural Modeling) to messy, real-world datasets.
  • Self-Starter Mentality: Experience functioning in "underdefined" spaces. As our first economist, you must be comfortable setting the roadmap.
  • Communication: The ability to explain not just the "what," but the "why" and the "what if." You can communicate uncertainty and risk to a CFO just as clearly as you can discuss model architecture with an ML Engineer.
  • Preferred Qualifications:
    • Prior experience in Fintech, Payments, or Travel industries.
    • Experience building and scaling "first-of-their-kind" functions within a data organization.

About Navan

Sourced by ZipRecruiter

Industry

Traveler accommodation

Company size

1,001 - 5,000 Employees

Headquarters location

Palo Alto, CA, US

Year founded

2015

Social media

Twitter

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