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Causal Inference Machine Learning Postdoctoral Jobs in Fort Worth, TX

Fairygodboss

Quant Analytics Associate Sr - DART

Plano, TX · On-site

$95 - $135/hr

Working knowledge of machine learning and applied statistics for operations use cases (time series ... causal inference frameworks). * Modern data engineering practices: orchestration (Airflow or ...

New

AppFolio

Sr. Machine Learning Engineer

Richardson, TX · On-site

$94K - $129K/yr

Who We Are Looking For We're hiring a Senior Machine Learning Engineer to design and ship the next ... Optimize for Voice: Drive selective Small Language Model (SLM) fine-tuning and inference ...

AppFolio

Sr. Machine Learning Engineer

Richardson, TX · On-site

$94K - $129K/yr

Who We Are Looking For We're hiring a Senior Machine Learning Engineer to design and ship the next ... Optimize for Voice: Drive selective Small Language Model (SLM) fine-tuning and inference ...

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Causal Inference Machine Learning Postdoctoral information

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

As of Sep 3, 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

Quant Analytics Associate Sr - DART

JPMorgan Chase & Co.

Plano, TX • On-site

$95K - $135K/yr

Full-time

Medical, Retirement

Re-posted 4 days ago

Key responsibilities

  • Own end-to-end MIS solution delivery, including requirements gathering, metric definition, data acquisition, modeling, transformation, validation, and visualization.

  • Design and build reliable ELT/ETL pipelines using Python and SQL, and implement orchestration, version control, and CI/CD processes.

  • Create executive-ready dashboards and self-service data marts with clear metrics and intuitive user experience.

JPMorgan Chase & Co. rating

7.9

Company rating: 7.9 out of 10

Based on 500 frontline employees who took The Breakroom Quiz

77th of 174 rated banks

Job description


Join our team as a senior quantitative analytics associate, where you'll collaborate with business partners to design innovative, automated solutions using cutting-edge technologies, driving operational efficiency in a dynamic, learning-focused environment.
As a Quant Analytics Associate Senior within DART (Data, Analytics and Reporting Team), you will play a crucial role in the DART MIS (Management Information System) setup and will be tasked with delivering effective business solutions. You will collaborate closely with various stakeholders and management levels to ensure the delivery of the most optimal solutions. As a member of the DART team you will leverage a broad technology suite to implement automated solutions and deliver data driven insights.
Job Responsibilities
  • Own end-to-end MIS solution delivery: requirements gathering, metric definition, source data acquisition, modeling, transformation, validation, and visualization.
  • Design and build reliable ELT/ETL pipelines in Python/SQL; implement orchestration, version control, and CI/CD to ensure repeatability and resilience.
  • Create executive-ready dashboards and self-service data marts (e.g., Tableau) with intuitive UX and clear metric definitions.
  • Apply advanced analytics (forecasting/time series, anomaly detection, segmentation, queuing/capacity planning) to optimize operational performance
  • Implement data quality frameworks (unit/integration tests, validation checks, anomaly monitoring), define SLAs/SLOs, and maintain runbooks.
  • Translate complex findings into concise narratives for senior stakeholders; influence decisions with data-backed recommendations.
  • Identify risks opportunities proactively, and value-unlock levers in operational processes; drive innovation in data management and automation.
  • Manage the book of work, prioritize initiatives, and deliver projects on time; lead cross-functional teams as SME and mentor junior colleagues.
  • Adhere to data governance, privacy, and control standards; ensure audit readiness, reproducibility, and clear documentation.

Required Qualifications, Capabilities, and Skills
  • 5+ years of hands-on analytics experience delivering measurable business improvements, with a strong track record in operations analytics or MIS within complex environments.
  • Bachelor's degree in a quantitative or technical field (Economics, Engineering, Physical Sciences, Mathematics, Operations Research, Statistics, Computer Science).
  • Expert-level SQL (complex joins, window functions, CTEs, performance tuning) and strong Python (pandas, NumPy; unit testing with pytest; structured logging; packaging).
  • Proven experience building automated data pipelines and operating in data lake/cloud environments (Snowflake; AWS services such as S3, Glue, Lambda; or equivalent).
  • Strong data visualization experience (Tableau or equivalent), including KPI design, dashboard UX, and audience-specific storytelling.
  • Working knowledge of machine learning and applied statistics for operations use cases (time series forecasting, supervised/unsupervised methods, feature engineering).
  • Familiarity with data wrangling tools (e.g., Alteryx) and, as applicable, R for statistical analysis.
  • Excellent verbal and written communication skills-able to synthesize complex analyses into concise executive narratives and visuals.
  • Demonstrated ability to collaborate across functions and levels; influence decisions; and drive adoption of data solutions.

Preferred Qualifications, Capabilities, and Skills
  • Banking industry experience and domain knowledge in Consumer & Community Bank (CCB) Operations (e.g., servicing/contact centers, payments/claims, fraud/disputes, collections) and workforce/capacity planning.
  • Experience with experimentation and causal methods (A/B testing design, uplift modeling, causal inference frameworks).
  • Modern data engineering practices: orchestration (Airflow or equivalent), transformation frameworks (dbt), API integrations, and containerization (Docker) or comparable tooling
  • Performance and cost optimization in cloud data platforms; query/profile tuning for large-scale datasets.
  • Exposure to risk and control frameworks; model documentation; audit and lineage standards in regulated environments.
  • Certifications (e.g., AWS Data/Analytics, Snowflake, Tableau).

About Us
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans
About the Team
Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.

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