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Causal Inference Jobs in Pittsburgh, PA (NOW HIRING)

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Causal Inference information

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$53.4K

$96.3K

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How much do causal inference jobs pay per year?

As of Aug 20, 2026, the average yearly pay for causal inference in Pittsburgh, PA is $96,335.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,500.00 and $105,300.00 per year, depending on experience, location, and employer.

What is a causal inference?

A Causal Inference job involves using statistical and computational methods to determine cause-and-effect relationships from data. Professionals in this field work with observational and experimental data to identify causal impacts, often in domains like economics, healthcare, social sciences, and technology. They apply techniques such as propensity score matching, instrumental variables, and difference-in-differences to ensure rigorous analysis. These roles are commonly found in academia, policy research, and data science teams within tech and finance companies. Strong skills in statistics, programming (e.g., Python, R), and experimental design are typically required.

What skills and qualifications are needed for a causal inference position?

Success in a Causal Inference role requires strong statistical knowledge, expertise in experimental and quasi-experimental methodologies, and advanced proficiency in programming languages like R or Python, typically acquired with an advanced degree in statistics, economics, data science, or a related field. Familiarity with specialized statistical software (such as Stata, SAS, or causal inference packages in R/Python), as well as experience with large datasets and machine learning tools, is highly valued. Excellent problem-solving abilities, clear communication, and collaboration skills are essential soft skills for effectively conveying complex findings to diverse teams. These competencies are critical to producing reliable insights that guide evidence-based decision-making in business, healthcare, or policy settings.

What are common challenges faced in a causal inference position?

Professionals in Causal Inference often encounter challenges such as dealing with confounding factors, addressing selection bias, and ensuring the validity of assumptions behind statistical models. They must carefully design experiments or leverage observational data while staying vigilant about potential data quality issues and model limitations. Collaboration with subject matter experts, data engineers, and business stakeholders is common to ensure accurate contextualization of results. Overcoming these challenges requires a mix of technical acumen and strong communication skills to translate complex analyses into actionable recommendations.

What are the most commonly searched types of Causal Inference jobs in Pittsburgh, PA?

The most popular types of Causal Inference jobs in Pittsburgh, PA are:

What job categories do people searching Causal Inference jobs in Pittsburgh, PA look for?

The top searched job categories for Causal Inference jobs in Pittsburgh, PA are:

Causal Inference jobs near you
Infographic showing various Causal Inference job openings in Pittsburgh, PA as of August 2026, with employment types broken down into 80% Full Time, 18% Part Time, and 2% Contract. Highlights an 72% Physical, 4% Hybrid, and 24% Remote job distribution, with an average salary of $96,335 per year, or $46.3 per hour.

Marketing Data Scientist, Measurement & Modeling

HDJ & Associates, Inc.

Pittsburgh, PA • On-site

Full-time

Re-posted 6 hours ago

Job description

Our client is seeking a highly analytical, curious, and results-driven marketing data scientist to join our marketing team. In this role, you will help shape marketing and commercial strategy through advanced analytics, machine learning, experimentation, and modern AI-enabled tools. A critical focus of this position is solving the "Online-to-Offline" puzzle by developing sophisticated models to connect digital engagement with physical outcomes across our national network of retail branches and service centers.

You will work across a wide range of initiatives, including customer acquisition, retention, segmentation, promotion optimization, forecasting, and marketing effectiveness. The ideal candidate brings strong statistical and technical skills, a practical business mindset, and the ability to translate complex data into actionable recommendations. This role will also help advance use of modern data science capabilities, including cloud-based modeling, ML workflows, generative AI tools, and privacy-aware measurement.

This role focuses on measurement, experimentation, and analytical decision partnership rather than day-to-day media buying or primary ownership of marketing-platform administration. Experimentation & Measurement: Lead test design and analysis, including A/B testing, market selection, incrementally testing, media mix modeling, holdout design, and final readouts to measure business impact. Business Partnership: Collaborate closely with marketing, ecommerce, and other digital business stakeholders to solve problems, identify opportunities, and drive data-informed decisions.

Reporting and Analytics: Establish and maintain reporting and performance review KPIs such as ROAS, CPA, LTV, unit economics and contribution metrics across both customer acquisition and retention. Data & Platform Integration: Partner with IT teams to define data requirements, validate data quality, and enable reliable datasets from GA4/BigQuery and advertising platforms for analysis, measurement, and decision-making. Predictive & Analytical Modeling: Develop and adapt predictive, statistical, and optimization models to support initiatives across customer acquisition, activation, engagement, retention, promotion planning, and channel performance.

AI & Advanced Analytics: Apply modern AI and machine learning techniques, including LLM and generative AI tools where appropriate, to improve analysis, workflow efficiency, insight generation, and decision support. Data Science Execution: Execute end-to-end projects by scoping business objectives, designing analytical approaches, building models, validating results, and delivering measurable solutions. Insight Communication: Communicate findings clearly through visualizations, presentations, and written summaries, translating complex analyses into practical business recommendations.

Requirements 3+ years of relevant professional experience applying advanced analytics or data science to business problems with measurable impact. Strong knowledge of statistical methods, hypothesis testing, regression analysis, causal inference, forecasting, and experimental design. Demonstrated ability to independently scope, prioritize, and deliver analytical work with limited day-to-day technical oversight.

Hands-on experience working with large, complex datasets and building predictive models in cloud or distributed computing environments. Proficiency in Python and SQL for data analysis, modeling, and workflow development. Experience with cloud-hosted data platforms such as Google Cloud Platform, AWS, or similar environments.

Familiarity with marketing analytics, customer behavior analysis, campaign measurement, customer segmentation, and customer lifecycle concepts. Comfort collaborating with IT on tracking and data pipelines, including server-side tracking, conversion APIs, and Online-to-Offline measurement. Experience translating ambiguous business questions into structured analytical plans and practical solutions.

Proven ability to learn quickly and leverage emerging marketing technologies, particularly AI-based measurement and optimization models. Strong communication skills, with the ability to work cross-functionally and explain technical concepts to business stakeholders. Bachelor's degree in Data Science, Statistics, Computer Science, Engineering, Economics, Operations Research, or a related field; advanced degree preferred.

PREFERRED QUALIFICATIONS Experience supporting ecommerce, retail, distribution, or other customer-focused commercial organizations. Experience with marketing measurement, attribution, incrementally testing, media optimization, or promotion analytics. Experience measuring marketing impact across both digital and offline outcomes, including branch visits, phone calls, and offline revenue.

Experience building reusable analytical workflows and collaborating IT to operationalize high-value outputs. Familiarity with web tracking, digital analytics, and modern measurement frameworks such as GA4, conversion APIs, or privacy-aware analytics environments. Experience with AI tools and/or machine learning systems in real-world business settings.

Knowledge of data visualization and BI platforms such as Power BI, Looker, or similar tools.

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