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Contract Causal Inference Jobs (NOW HIRING)

fphny

Sr. Research Advisor

Astoria, NY · On-site +1

$100/hr

Expertise in quantitative research methods, including causal inference (e.g. propensity score ... This does not constitute a contract of employment. Residency Requirement: You must live in the New ...

1 point system

Data Engineer

Los Angeles, CA · On-site

$60/hr

... Contract Rate $60/hr. on W2 * Serve as developer of key DTC analytical models like customer ... testing, and causal inference. * Strong skills in feature engineering, handling large-scale ...

Haus

Staff Engineer - Data Platform - Seattle

Seattle, WA · On-site

$240K - $260K/yr

Using frontier causal inference-based econometric models to run experiments, we help brands measure ... Design and lead implementation of high-leverage systems: schema evolution, data contracts, DQ ...

Master Electronics

Data Scientist II

Phoenix, AZ

Apply causal inference techniques (uplift modeling, difference-in-differences, synthetic controls ... Experience with B2B or ecommerce pricing, such as quote optimization, contract pricing, or price ...

Instacart

Ads AI Analytics Lead II

OR · Remote

Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs ... and causal inference. * Hands-on experience with workflow automation and low-code development ...

Master Electronics

Data Scientist II

Phoenix, AZ

Apply causal inference techniques (uplift modeling, difference-in-differences, synthetic controls ... Experience with B2B or ecommerce pricing, such as quote optimization, contract pricing, or price ...

Master Electronics

Data Scientist II

Phoenix, AZ · On-site

Apply causal inference techniques (uplift modeling, difference-in-differences, synthetic controls ... Experience with B2B or ecommerce pricing, such as quote optimization, contract pricing, or price ...

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All JobsContract Causal Inference Jobs

Contract Causal Inference information

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 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, and why are they important?

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 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.

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Contract Causal Inference jobs near you
Director, Medical Analytics and Exploratory Data Science

Director, Medical Analytics and Exploratory Data Science

Revolution Medicines

Redwood City, CA • Hybrid

Other

Re-posted 2 days ago

Job description

The Opportunity:

We are seeking a highly motivated and scientifically rigorous Director of Biostatistics to our Medical Analytics and Exploratory Data Science Biostatistics group. This role will provide strategic and hands-on statistical leadership for exploratory data analyses, scientific publications, real-world evidence (RWE), post-marketing research, and health economics and outcomes research (HEOR) initiatives. The successful candidate will serve as a key statistical leader and individual contributor, partnering closely with cross-functional teams to generate high-quality evidence that advances our oncology pipeline and supports medical and scientific strategy.

  • Provide statistical leadership for exploratory data analyses using existing clinical trial data, real world data studies, post-marketing research, and HEOR projects.

  • Serve as a primary statistical contact for assigned projects, working collaboratively with clinical development, medical affairs, safety, statistical programming, regulatory affairs and commercial.

  • Lead the design, analysis, and interpretation of complex statistical models, including survival analysis, machine learning, and casual inference methodologies.

  • Contribute to and implement policies, standards, and procedures to ensure consistency and quality in statistical practices.

  • Manage relationships with external partners, such as contract research organizations (CROs), ensuring adherence to timelines, budgets, and quality standards.

  • Mentor and provide technical guidance to junior statisticians, fostering scientific rigor, innovation, and professional growth.

  • Contribute to regulatory and payers/HTA agencies interactions, scientific publications, abstracts, and internal decision-making through clear and effective communication of statistical results.

Required Skills, Experience and Education:

  • Ph.D. or M.S. in Statistics/Biostatistics, a minimum of 8 years (for Ph.D.) and 12 years (for M.S.) of experience in biotech/pharma industry as a statistician.

  • Solid knowledge of statistical methodologies for oncology, including survival analysis and causal inference.

  • Hands-on experience in exploratory analysis of oncology trials.

  • Proven ability to independently lead statistical aspects of complex, cross-functional projects.

  • Strong understanding of regulatory requirements related to biostatistical activities and clinical trials.

  • Excellent verbal and written communication skills are required.

  • Excellent interpersonal and project management skills are essential.

  • Proficiency in SAS and/or R.

Preferred Skills:

  • Knowledge of RWD and health economics and outcomes research (HEOR) in oncology is a plus.

  • Familiarity with machine learning or advanced modeling approaches applied to biomedical or observational data. 

    #LI-Hybrid  #LI-SH1

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