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Manager Causal Inference Jobs in Houston, TX (NOW HIRING)

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities

Principal AI/ML Software Engineer

Houston, TX · On-site

$128K - $172K/yr

... management platform. This role requires a technical expert who can develop, deploy, and maintain ML ... Strong foundation in statistics, A/B testing, causal inference, and experimental design • ...

... management platform. This role requires a technical expert who can develop, deploy, and maintain ML ... Strong foundation in statistics, A/B testing, causal inference, and experimental design ...

Ability to manage multiple priorities in a fast-paced consulting environment. * Experience with HIV ... Experience with survey data, weighting methodologies, causal inference, or advanced statistical ...

Manager Causal Inference information

See Houston, TX salary details

$27.7K

$99.9K

$112.7K

How much do manager causal inference jobs pay per year?

As of Aug 23, 2026, the average yearly pay for manager causal inference in Houston, TX is $99,867.00, according to ZipRecruiter salary data. Most workers in this role earn between $108,900.00 and $111,300.00 per year, depending on experience, location, and employer.

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.

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 are the most commonly searched types of Causal Inference jobs in Houston, TX?

The most popular types of Causal Inference jobs in Houston, TX are:

What are popular job titles related to Manager Causal Inference jobs in Houston, TX?

For Manager Causal Inference jobs in Houston, TX, the most frequently searched job titles are:

What job categories do people searching Manager Causal Inference jobs in Houston, TX look for?

The top searched job categories for Manager Causal Inference jobs in Houston, TX are:

Full-time

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Job description

Required Skills
  • 10+ years of hands-on experience in Applied Data Science, Analytics Engineering, and Systems Modeling.
  • 5+ years of client-facing, consulting, or business development experience delivering analytics solutions.
  • Expertise in statistical modeling, machine learning, and predictive analytics.
  • Strong proficiency with Python, scikit-learn, statsmodels, PyTorch, and TensorFlow.
  • Experience in time-series forecasting, causal inference, feature engineering, and advanced data sampling/resampling techniques.
  • Strong expertise in geospatial analytics and LiDAR data processing.
  • Hands-on experience with ArcGIS, PostGIS, GeoPandas, GDAL/OGR, PDAL, and related geospatial libraries.
  • Experience working with vector, raster, point-cloud, and sensor datasets.
  • Excellent analytical, communication, and stakeholder management skills.
Roles & Responsibilities
  • Design and develop advanced machine learning and statistical models to solve complex business problems.
  • Build predictive models, time-series forecasting solutions, and causal inference frameworks.
  • Perform feature engineering, data preparation, and advanced sampling techniques for large-scale datasets.
  • Develop geospatial analytics and LiDAR processing solutions using industry-standard tools and libraries.
  • Analyze vector, raster, point-cloud, and sensor data to generate actionable insights.
  • Partner with business stakeholders to scope, design, and deliver data science solutions.
  • Present analytical findings and recommendations to technical and business audiences.
  • Optimize model performance, scalability, and deployment in production environments.
  • Mentor data scientists and promote best practices in analytics and machine learning.
  • Support innovation initiatives through advanced analytics and AI-driven solutions.