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Manager Causal Inference Jobs in New York (NOW HIRING)

What you'll be doing * Design, build, and productionize a causal inference platform that ... Work cross-functionally with Data Engineering, Product Management, and Core Analytics to translate ...

What you'll be doing * Design, build, and productionize a causal inference platform that ... Work cross-functionally with Data Engineering, Product Management, and Core Analytics to translate ...

Senior Product Manager

Manhattan, NY · On-site

$120 - $150/hr

This is a builder role for someone who is comfortable reasoning about causal inference ... management or data-heavy product work, including time owning measurement, ML‑adjacent, or ...

Senior Product Manager

New York, NY · On-site

$138K - $182K/yr

This is a builder role for someone who is comfortable reasoning about causal inference ... management or data-heavy product work, including time owning measurement, ML-adjacent, or data ...

Deep expertise in the core methods: media mix modeling, multi-touch attribution, incrementality and lift testing, and causal inference at scale. * Experience managing or technically leading a small ...

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Manager Causal Inference information

How does a manager 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 causal inference?

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 New York? The most popular types of Causal Inference jobs in New York are:
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Re-posted 10 days ago


Job description

Title: Data Scientist

location: NYC, NY (Hybrid)

Full Time

Technical Responsibilities:

  • Design and Execute Experiments: Lead end-to-end A/B testing initiatives and Geo Experiments, from hypothesis formation and experimental design to statistical analysis and business recommendations
  • Advanced Statistical & Causal Inference: Apply deep knowledge of experimental design, regression, classification, causal inference (difference-in-differences, propensity scores, instrumental variables), and ensure proper assumptions.
  • Build Scalable Solutions: Develop experimentation and causal inference tools and frameworks that can scale across Disney's businesses
  • Deliver Strategic Insights: Partner with stakeholders to identify optimization opportunities and translate complex analytical findings into clear business recommendations
  • Influence Executive Decisions: Present findings and recommendations to senior leadership, effectively communicating statistical concepts to non-technical stakeholders

Basic Qualifications:

  • Bachelors in Statistics, Economics, Computer Science, Engineering, Mathematics, Physics, or a related field + 7 years of experience with an emphasis on experimentation or causal inference.
  • Strong background in statistical modeling: regression, classification, time series forecasting, causal inference, and other techniques.
  • Robust knowledge of causal inference approaches such as propensity scores, synthetic controls, difference-in-differences, doubly robust methods, meta learners, and uplift modeling.
  • Expertise in A/B test design, execution, statistical modeling, and sophisticated causal inference techniques.
  • Proficient in conducting sample size calculations, power analysis, and minimum detectable effect estimation.
  • Experience managing multiple testing scenarios and controlling false discovery rates.
  • Ability to deploy both Bayesian and frequentist statistical approaches.
  • Deep understanding of assumptions required for causal inferences, including the foundational statistical concepts that underpin the approaches.
  • Proven ability to manage end-to-end experimentation and causal inference analyses, from initial requirements to impactful outcomes
  • Advanced skills in Python and/or R including development of statistical analysis packages, and use of ML frameworks (e.g., scikit-learn, LGBM).
  • Strong communication skills for translating complex data into actionable narratives and presenting confidently to technical and non-technical audiences, including senior executives.

Qualifications:

  • MS in computer science, statistics, math or a related quantitative field 10+ years of relevant experience OR PhD + 3 years of relevant experience with an emphasis on experimentation or causal inference.
  • Experience with ETL and data engineering: data extraction, transformation, integration, and quality controls for analytics at scale.
  • Skilled in production deployment and monitoring of data science solutions, including CI/CD pipelines, automated reporting, and ongoing experiment/model monitoring.
  • Familiarity with data platforms and applications such as Databricks, Jupyter, Snowflake, and Github.
  • Strong strategic business insight, preferably in subscription-based business models, with ability to apply experimentation and analytics to market trends and consumer insights.
  • Proven track record of leadership and stakeholder/project management, including influencing cross-functional teams and delivering high-impact outcomes.
  • Adept at adapting quickly to shifting priorities in a fast-moving environment while maintaining quality.
  • Drive and maintain a culture of quality, innovation and experimentation.
  • Demonstrated experience mentoring colleagues on best practices and technical concepts for building large scale solutions.