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Causal Inference Machine Learning Postdoctoral Jobs in New York

Ramp

Senior Applied Scientist, Credit Risk

New York, NY · Remote

$165K - $228K/yr

Apply methods from machine learning, statistics, causal inference, optimization, and economics to solve core business problems * Generate and communicate data-driven insights that influence product ...

Ramp

Senior Applied Scientist, Credit Risk

New York, NY · On-site

$165K - $228K/yr

Apply methods from machine learning, statistics, causal inference, optimization, and economics to solve core business problems * Generate and communicate data-driven insights that influence product ...

Mount Sinai Hospital

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$53K - $73K/yr

... machine-learning/deep-learning methodology research with application to biomedical data. • ... and causal-inference methodology research with application to medical/clinical-trial studies. The ...

Mount Sinai Hospital

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$53K - $73K/yr

... machine-learning/deep-learning methodology research with application to biomedical data. • ... and causal-inference methodology research with application to medical/clinical-trial studies. The ...

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Causal Inference Machine Learning Postdoctoral information

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, and why are they important?

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.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in New York? For Causal Inference Machine Learning Postdoctoral jobs in New York, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in New York look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in New York are:
What cities in New York are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities in New York with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you

Research Engineer, Machine Learning

Basis Research

New York, NY • On-site

$120K - $180K/yr

Full-time

Re-posted 3 days ago

Job description

About Basis
Basis is a nonprofit applied AI research organization with two mutually reinforcing goals.
The first is to understand and build intelligence. This means to establish the mathematical principles of what it means to reason, to learn, to make decisions, to understand, and to explain; and to construct software that implements these principles.
The second is to advance society's ability to solve intractable problems. This means expanding the scale, complexity, and breadth of problems that we can solve today, and even more importantly, accelerating our ability to solve problems in the future.
To achieve these goals, we're building both a new technological foundation that draws inspiration from how humans reason, and a new kind of collaborative organization that puts human values first.
About the Role
Research engineers support Basis' mission by translating research ideas into correct, robust, and scalable high-quality code.
We seek individuals who excel technically and value probing concepts at their foundations. Our research engineers aspire to conduct rigorous, high-quality, robust science, unafraid to tinker, make mistakes, and explore radically different ideas to achieve this.
Basis is a collaborative endeavor, both internally and with our external partners; we seek individuals who relish working with others on challenges larger than those they can tackle alone.
Machine Learning Research Engineers
This role targets experts in machine learning engineering. The core areas of ML research engineering include:
  • Probabilistic programming and statistical inference
  • Deep learning
  • Causal inference
  • Program synthesis and analysis
  • ML Ops and systems engineering

These areas are honed within the context of building reasoning systems. Consequently, research engineers will also engage with topics such as programming language design and implementation, automatic differentiation, and SAT/SMT solvers, among others.
We expect you to:
  • Possess excellent programming and software engineering skills, especially in Julia, Python, C++, ML-family languages.
  • Have demonstrated the ability to drive software projects from start to finish. This could be evidenced by open-source projects, technical reports, and publications.
  • Be comfortable digesting research from PL and/or ML venues, such as PLDI, POPL, NeurIPS, or ICML.
  • Progress with a high degree of autonomy and under uncertainty.
  • Be enthusiastic about solving real-world problems and making a positive societal impact.
  • Have demonstrated significant technical achievements within ML engineering. Examples include:
    • You've implemented variants of newly published techniques from scratch.
    • You built systems and workflows for training large models distributed across many machines.
    • You've built systems that span all levels of the programming stack from high-level API infrastructure to close-to-the-metal code.

In addition, the following would be an advantage:
  • A PhD (or equivalent experience) in technical areas including: statistics, programming languages, machine learning, computational neuroscience, cognitive science, physics, mathematics.

Responsibilities:
  • Translate research ideas into correct, robust, and scalable high-quality code.
  • Engage in programming language design/implementation.
  • Performance engineering, scaling research code.
  • Algorithm development.
  • Contribute to the culture and direction of Basis.
  • (Optionally) Publish and present findings in journals and conferences.
Role Details
Exceptional candidates who may not meet all of the following criteria are still encouraged to apply.
  • FT/PT: This is a full-time position
  • Hours: While we prioritize in-person collaboration for its benefits to creative work, there is a degree of flexibility in your working hours. Be prepared to attend multi-day Basis-wide in-person events.
  • Location: This role is in-person in either New York City or Boston.
  • Salary range: Competitive salary and bonuses

Non-Discrimination Notice
Basis Research Institute provides equal employment opportunities without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or genetics and prohibits discrimination based on all protected characteristics.
Privacy Notice
By submitting your application, you grant Basis permission to use your materials for both hiring evaluation and recruitment-related research and development purposes. Your information may be processed in different countries, including the US. You retain copyright while providing Basis a license to use these materials for the stated purposes.
Read our full Global Data Privacy Notice here.

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