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Contract Causal Inference Jobs in Austin, TX (NOW HIRING)

Varsity Tutors

LSAT Logical Reasoning Tutor

Austin, TX · Remote

$26 - $40/hr

... inference questions. Ability to explain argument structure, conditional logic, causal reasoning ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

Varsity Tutors

LSAT Logical Reasoning Tutor

San Marcos, TX · Remote

$26 - $40/hr

... inference questions. Ability to explain argument structure, conditional logic, causal reasoning ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

Varsity Tutors

LSAT Logical Reasoning Tutor

Round Rock, TX · Remote

$26 - $40/hr

... inference questions. Ability to explain argument structure, conditional logic, causal reasoning ... Varsity Tutors does not contract in: Alaska, California, Colorado, Delaware, Hawaii, Maine, New ...

All JobsContract Causal Inference JobsContract Causal Inference Jobs in Austin, TX

Contract Causal Inference information

What is a contract causal inference specialist?

A Contract Causal Inference specialist is a professional who applies statistical and analytical methods to determine cause-and-effect relationships within data, typically on a contractual or project basis. These specialists are often brought in to analyze business, healthcare, or social science data to help organizations make evidence-based decisions. They use techniques such as randomized controlled trials, regression analysis, and propensity score matching to isolate causal impacts. Contract roles are usually temporary and focused on specific projects or questions. This position requires strong statistical knowledge, programming skills, and the ability to communicate findings to non-technical stakeholders.

What are the key skills and qualifications needed to thrive as a contract causal inference specialist?

To thrive as a Contract Causal Inference Specialist, you need a strong background in statistics, econometrics, or data science, typically with an advanced degree in a quantitative field. Proficiency with statistical software like R, Python, and specialized causal inference packages, as well as experience with data wrangling tools, is essential. Exceptional analytical thinking, clear communication, and attention to detail are valuable soft skills for interpreting results and collaborating with clients. These competencies are vital for delivering robust, actionable insights that drive evidence-based decision-making in a contractual setting.

What are some common challenges faced by professionals in contract causal inference roles, and how can they be addressed?

Professionals in contract causal inference roles often encounter challenges such as working with incomplete or messy datasets, ensuring the validity of assumptions in causal models, and effectively communicating complex findings to stakeholders. Addressing these issues typically involves using robust statistical techniques, performing thorough data cleaning, and engaging in transparent documentation of the modeling process. Additionally, collaborating closely with subject matter experts and stakeholders can help clarify project goals and improve the relevance and impact of your analyses.

What is the difference between Contract Causal Inference vs Data Analyst?

AspectContract Causal InferenceData Analyst
Required CredentialsStatistics, Data Science, or related certifications; often advanced degreesBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch-focused, project-based, often in consulting or academiaBusiness environments, analyzing data to inform decisions
Employer & Industry UsageResearch institutions, consulting firms, tech companiesCorporations, marketing agencies, finance, healthcare
Search & Comparison IntentUnderstanding causal relationships, research projectsData analysis, reporting, business insights

Contract Causal Inference specialists focus on identifying cause-and-effect relationships through research and statistical methods, often in consulting or academic settings. Data Analysts interpret data to generate reports and insights for business decisions. While both roles require data skills, Contract Causal Inference emphasizes causal modeling and research, whereas Data Analysts focus on descriptive and diagnostic analysis.

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

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

What are popular job titles related to Contract Causal Inference jobs in Austin, TX?

For Contract Causal Inference jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching Contract Causal Inference jobs in Austin, TX look for?

The top searched job categories for Contract Causal Inference jobs in Austin, TX are:

What cities near Austin, TX are hiring for Contract Causal Inference jobs?

Cities near Austin, TX with the most Contract Causal Inference job openings:

Contract Causal Inference jobs near you
Infographic showing various Contract Causal Inference job openings in Austin, TX as of August 2026, with employment types broken down into 79% Full Time, 18% Part Time, and 3% Contract. Highlights an 69% Physical, 3% Hybrid, and 28% Remote job distribution.

Senior AI/ML Ops Engineer Agent Evaluation, Observability & Production Reliability

iPeople Infosystems LLC

Austin, TX • On-site

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Job Title: Senior AI/ML Ops Engineer Agent Evaluation, Observability & Production Reliability
Location: Cupertino, CA or Austin, TX (onsite)
Type: Contract Position
Job Description

Must Have

  • 5+ years of experience in ML engineering, MLOps, platform engineering, or SRE, including 2+ years working hands-on with LLMs or LLM-powered applications in production.
  • Demonstrated experience building evaluation systems for ML or LLM applications: test harnesses, benchmark datasets, automated scoring (including LLM-as-judge approaches), and regression detection.
  • Strong software engineering skills in Python (and ideally TypeScript), with a track record of building reliable, well-tested internal platforms and tooling.
  • Deep familiarity with CI/CD systems (e.g., GitHub Actions, GitLab CI, Jenkins, Buildkite) and experience embedding automated quality gates into deployment pipelines.
  • Experience with observability and monitoring stacks (e.g., OpenTelemetry, Datadog, Grafana/Prometheus) and, ideally, LLM-specific observability tools (e.g., LangSmith, Langfuse, Arize Phoenix, Braintrust, W&B Weave).
  • Proven ability to debug complex distributed systems under pressure, including production incident response, root-cause analysis, and blameless postmortems.
  • Excellent cross-functional communication: able to translate evaluation results into clear findings and recommendations for both engineers and non-technical stakeholders.
  • Comfort with ambiguity and a builder s mindset: this role starts with a blank page and ends with the evaluation platform the whole organization relies on.
  • Experience with agentic frameworks and orchestration patterns (e.g., multi-agent systems, tool use, RAG pipelines) and their distinct failure modes.
  • Experience with prompt management, model routing, or fine-tuning workflows and evaluating changes across model versions and providers.
  • Background in statistics or experimentation (A/B testing, significance testing, sampling strategies for human review).
  • Design and build reusable AI agent skills, plugins and maintain internal marketplace infrastructure to extend and scale Data, AIML capabilities across the organization.
  • Expertise in causal inference and measurement strategy including causal graphs, ontologies, and knowledge graphs to drive rigorous, decision grade data analysis.
  • Experience operating in regulated or high-stakes domains where agent errors carry real business or customer impact.

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