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Causal Inference Machine Learning Postdoctoral Jobs in Fort Worth, TX

AT and T

Director-Advanced Analytics

Dallas, TX · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

By integrating Marketing Mix Models (MMM), causal inference, experimentation, predictive analytics ... AI, machine learning, and next-generation causal modeling techniques Scale Marketing ...

Publicis Groupe Holdings B.V

Data Scientist

Plano, TX · On-site

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design * Experience in media performance analytics such as attribution modeling ...

Publicis Groupe Holdings B.V

Data Scientist

Plano, TX

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Leverage machine learning, causal inference methods or statistically sound analyses to drive actionable insights and help shape our understanding of marketing strategy, campaign planning, efficacy ...

UNAVAILABLE

Machine Learning Engineer

Frisco, TX · On-site

$140 - $190/hr

Contribute to our machine learning repositories and optimize models for performance, scalability, and real‑time inference across edge and cloud environments. * Drive performance optimization and ...

Noblesoft Technologies

Gen AI Lead

Dallas, TX · On-site

$138K - $170K/yr

Machine learning development lifecycle - (Data preparation, Data visualization, Statistical ... AI, Causal Inference, Time series analysis, Forecasting, Anomaly detection, Hypothesis testing, A/B ...

ClifyX

Senior AI/ML Developer

Irving, TX · On-site

The role involves building and deploying machine learning pipelines, developing GenAI solutions ... causal inference. • ONNX/TensorRT model optimization and real time inference experience. • ...

Quarterhill

Machine Learning Engineer

Frisco, TX · On-site

  • Medical

  • Dental

  • Retirement

  • PTO

Contribute to our machine learning repositories and optimize models for performance, scalability, and real-time inference across edge and cloud environments. * Drive performance optimization and ...

Doist

Machine Learning Engineer, II - 3D Perception

Fort Worth, TX · On-site

$153 - $184/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... inference workflows. Analyze model performance, identify failure modes, and independently ... Support and mentor Machine Learning Engineer I team members on implementation, experimentation, and ...

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Fort Worth, TX

Causal Inference Machine Learning Postdoctoral information

See Fort Worth, TX salary details

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$58.5K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Aug 17, 2026, the average yearly pay for causal inference machine learning postdoctoral in Fort Worth, TX is $51,967.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,300.00 and $54,100.00 per year, depending on experience, location, and employer.

What is a causal inference machine learning postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a causal inference machine learning postdoctoral researcher?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by causal inference machine learning postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

Is it difficult to get a causal inference machine learning postdoctoral position?

Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Fort Worth, TX?

For Causal Inference Machine Learning Postdoctoral jobs in Fort Worth, TX, the most frequently searched job titles are:

What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Fort Worth, TX look for?

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Fort Worth, TX are:

What cities near Fort Worth, TX are hiring for Causal Inference Machine Learning Postdoctoral jobs?

Cities near Fort Worth, TX with the most Causal Inference Machine Learning Postdoctoral job openings:

Causal Inference Machine Learning Postdoctoral jobs near you

Director-Advanced Analytics

AT and T

Dallas, TX • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 5 days ago

AT&T rating

7.3

Company rating: 7.3 out of 10

Based on 729 frontline employees who took The Breakroom Quiz

53rd of 99 rated telecommunications companies

Job description

The Director, Advanced Analytics sits at the forefront of AT&T's transformation into a truly evidence-driven organization. This role is responsible for uncovering the causal truth behind business performance, determining what actions create incremental impact, which investments drive growth, and where opportunities exist to accelerate customer and financial outcomes.
As the organization's leader in causal measurement and experimentation, the Director will pioneer the development of advanced causal models, experimentation frameworks, and analytical systems that uncover the true drivers of business performance. By integrating Marketing Mix Models (MMM), causal inference, experimentation, predictive analytics, and AI-enabled decision intelligence, this leader will establish the trusted source of causal truth for AT&T, revealing how media investments, offer strategies, sponsorships, and growth initiatives generate incremental business value and enabling leaders to make faster, smarter, and more confident investment decisions.
The Director owns the end-to-end measurement ecosystem, from defining strategic business questions and designing rigorous experiments to developing advanced analytical solutions, validating insights, and driving organizational adoption. Success requires challenging assumptions, uncovering hidden drivers of performance, and translating complex analytical findings into simple, decision-grade recommendations that influence strategy and investment decisions at the highest levels of the organization.
The ideal candidate is both a scientist and a strategist, equally comfortable designing sophisticated causal frameworks, applying advanced statistical and AI-driven methodologies, and influencing executive decisions. They are passionate about solving complex business problems, building stakeholder confidence in measurement outcomes, and creating a culture where decisions are driven by evidence, experimentation, and continuous learning.
This position requires office presence of a minimum of 5 days per week and is only located in the location(s) posted.
About the Marketing & Growth Organization
The Marketing & Growth Organization is AT&T's marketing engine, responsible for driving sustainable growth by deeply understanding and championing our customers, captivating them with compelling products, services and experiences; and building a culturally relevant and beloved brand. We pride ourselves in continuously striving to embody expertise, simplicity and inspiration in everything we do.
Marketing Measurement and Optimization (MMO)
Do you want to use your analytics skills to help craft strategy at AT&T? Are you interested in developing the cutting edge of the Marketing Data Analytics industry?
The mission of the MMO team is to develop and implement a systematic approach to quantify the holistic drivers of demand to understand the opportunity costs of investment decisions. We measure the impact media, offers, and all other drivers has on sales, profitability, brand equity, and consumer behavior.
Key Responsibilities:
Establish AT&T's Marketing Causal Truths
  • Develop AT&T's causal measurement architecture by integrating causal inference, experimentation, advanced econometrics, MMM, and AI-enabled analytics into a unified intelligence platform
  • Build scalable causal models that quantify incremental impact, identify business drivers, and predict outcomes across media, offers, sponsorships, and growth initiatives
  • Embed causal evidence into forecasting, optimization, and investment planning to improve resource allocation and business performance
  • Advanced measurement science capabilities through the application of AI, machine learning, and next-generation causal modeling techniques

Scale Marketing Experimentation and Learning
  • Lead Marketing's experimentation ecosystem, establishing governance, standards, and frameworks for incrementality measurement, causal validation, and Test & Learn programs
  • Design and oversee market-moving experiments including geo-test, holdouts, quasi-experimental methods, synthetic controls, and difference-in-differences to validate investment effectiveness
  • Develop experimentation roadmaps across media, AI-enabled search, sponsorships, targeting, influencers, messaging, and emerging growth channels
  • Continuously calibrate MMM and predictive models using causal evidence to improve forecast accuracy and investment recommendations
  • Accelerate organizational learning by transforming experimental results into repeatable business practices and scalable growth strategies

Transform Causal Evidence into Business Impact
  • Translate complex causal insights into decision-ready recommendations that optimize marketing investments and accelerate customer and financial growth
  • Influence strategic decisions through evidence-based storytelling, connecting measurement outcomes to business impact, investment tradeoffs, and growth opportunities
  • Build stakeholder confidence in measurement outcomes through rigorous validation, methodological transparency, and consistent delivery of trusted insights
  • Lead and develop and high-performing team of causal measurement experts, advancing capabilities in experimentation, causal models, AI-enabled analytics and marketing science
  • Establish causal measurement as a competitive advantage, enabling faster, smarter, and more confident business decisions

Requirements/Qualifications:
  • Deep expertise in causal measurement, developing and applying advanced causal methodologies including RCTs, geo experimentation, synthetic controls, difference-in-differences, regression discontinuity, Bayesian approaches, and causal machine learning.
  • You are equally comfortable translating causal assumptions into graphical and structural framework such as Directed Acyclic Graphs (DAGs) and Structural Causal Models (SCMs) to inform research design, identify bias, and strengthen causal interpretation.
  • Technical leader with strong scientific rigor, experienced developing, validating, and operationalizing causal frameworks that combine market tests, observational data, media experiments, Marketing Mix Models (MMM), forecasting systems and optimization models into trusted business recommendations.
  • Thought leader in measuring innovation, continuously advancing the application of causal AI, machine learning, experimental design, and emerging measurement methodologies to improve how the organization learns, predicts outcomes, and allocated resources.
  • Exceptional storyteller and executive influencers, translating complex analytical findings into compelling, decision-grade recommendations that drive action at the highest levels of the organization.
  • Builder of high-performing teams, fostering a culture of scientific curiosity, technical excellence, continuous learning, and measurable business impact while developing the next generation of measurement and analytics leaders.
  • Strategic partner and change agent, capable of influencing across highly matrixed organizations, aligning diverse stakeholders around evidence-based decisions, and building trust in measurement outcomes through methodological rigor and transparency.

Our Director-Advanced Analytics jobs earn between $210,600.00 - $316,000.00 USD Annual. Not to mention all the other amazing rewards that working at AT&T offers. Individual starting salary within this range may depend on geography, experience, expertise, and education/training.
Joining our team comes with amazing perks and benefits:
  • Medical/Dental/Vision coverage
  • 401(k) plan
  • Tuition reimbursement program
  • Paid Parental Leave
  • Paid Caregiver Leave
  • Additional sick leave beyond what state and local law require may be available but is unprotected
  • Adoption Reimbursement
  • Disability Benefits (short term and long term)
  • Life and Accidental Death Insurance
  • Supplemental benefit programs: critical illness/accident hospital indemnity/group legal
  • Employee Assistance Programs (EAP)
  • Extensive employee wellness programs
  • Employee discounts up to 50% off on eligible AT&T mobility plans and accessories, AT&T internet (and fiber where available) and AT&T phone
  • Long Term Grants and Deferred Compensation
  • Paid Time Off and Holidays (based on date of hire, at least 28 days of vacation each year and 9 company-designated holidays

Weekly Hours:
40
Time Type:
Regular
Location:
Dallas, Texas
Salary Range:
$210,600.00 - $316,000.00
AT&T and its subsidiaries are committed to equal employment opportunity. All hiring, promotion, and other employment decisions remain merit-based and free from discrimination on the basis of race, color, religion, religious creed, national origin, ancestry, age, sex, sexual orientation, gender, gender identity, gender expression, physical disability, mental disability, pregnancy, medical condition, genetic information, marital status, citizenship status, military status, veteran status, or any other characteristic protected by federal, state, or local laws. In addition, AT&T will provide reasonable accommodations to qualified individuals with disabilities. AT&T is a fair chance employer and does not initiate a background check until an offer is made. Click here to learn more or request an application accommodation here.

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