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Causal Inference Machine Learning Postdoctoral Jobs in New York

Ramp

Senior Applied Scientist, Credit Risk

New York, NY · On-site

$165K - $228K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

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 ...

Amazon

Economist, Ads Measurement Science

New York, NY · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Key job responsibilities Leverage deep expertise in causal inference to develop robust, causally ... machine-learning methods (e.g., boosted regression trees, random forests, neural networks ...

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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?

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:

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The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in New York are:

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Cities in New York with the most Causal Inference Machine Learning Postdoctoral job openings:

Causal Inference Machine Learning Postdoctoral jobs near you

Senior Applied Scientist, Credit Risk

Ramp

New York, NY • On-site

$165K - $228K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 4 days ago

Job description

About Ramp
Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books.
The problems are high-stakes, data-dense, and unforgiving.
We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you've built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome.
The median Ramp customer saves 5% and grows revenue 16% in their first year - far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same.
If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.
About the Role
We're looking for a Senior Applied Scientist to help drive the future of credit applied science at Ramp. In this role, you will design, build, and optimize the models that power our credit risk systems, helping us make faster, smarter, and more scalable risk decisions for our customers.
You'll work at the intersection of machine learning, statistics, economics, and product strategy. This role requires strong technical depth as well as close collaboration with business, product, data, and engineering partners. You will help identify high-impact opportunities, translate ambiguous business problems into rigorous modeling work, and ship models that operate reliably in production.
Applied scientists at Ramp focus on solving quantitative problems across credit, fraud, growth, and our core product by applying the right mix of machine learning, causal inference, structural modeling, and optimization.
What You'll Do
  • Design, build, and optimize machine learning models that support credit risk decisioning and portfolio management at Ramp
  • Own the full applied science development lifecycle, from data exploration and feature development to model prototyping, deployment, monitoring, and iteration
  • Investigate and evaluate new data sources, including structured and unstructured data, and integrate them into credit models where appropriate
  • Develop backtesting, validation, and monitoring frameworks to evaluate model performance and business impact
  • 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, risk, and company strategy
  • Partner with product, business, engineering, and data stakeholders to translate ambiguous problems into clear objectives, scoped opportunities, and a practical applied science roadmap
  • Contribute to best practices for model development, experimentation, documentation, testing, and production reliability

What You Need
  • Bachelor's degree or above in Math, Economics, Bioinformatics, Statistics, Engineering, Computer Science, or other quantitative fields.
  • 5+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 3+ years of industry experience with a PhD
  • Strong familiarity with the mathematical fundamentals of advanced statistics, machine learning, optimization, and/or economics
  • Experience working with large datasets using Python and SQL
  • Strong Python experience across exploratory data analysis, predictive modeling, and applied machine learning, using tools such as NumPy, pandas, scikit-learn, PyTorch, or similar libraries
  • Strong communication: the ability to bridge technical methodology to meaningful data narratives to drive company decisions and strategy
  • Track record of shipping high-quality machine learning products in production and at scale
  • Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions

Nice-to-Haves
  • PhD in Math, Economics, Bioinformatics, Statistics, Engineering, Computer Science, or other quantitative fields
  • Strong perspective on data science engineering development cycle (data modeling, version control, documentation + testing, best practices for codebase development)
  • Familiarity with data orchestration platforms (Airflow, Dagster, Prefect)
  • Experience at a high-growth startup
  • Experience leveraging AI/LLMs for development or for internal workflows

Benefits available to all full-time Ramp employees (Global)
  • Flexible PTO
  • Centralized home-office equipment ordering
  • Health and wellness stipend
  • Budget for intra-office travel
  • Weekly coffee stipend

United States
  • 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents
  • One Medical annual membership
  • 401(k), including employer match on contributions made while employed by Ramp
  • Fertility HRA (up to $10,000 per year)
  • Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay
  • Pet insurance
  • In-office perks: lunch, snacks, drinks, and more
  • Relocation support to NYC or SF (as needed)
Canada
  • Group medical, dental, and vision coverage through Sun Life
  • Life, AD&D, and disability coverage
  • Fertility drug coverage (up to $4,000 lifetime)
  • Group Retirement Plan with employer match (RRSP + DPSP)
  • Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay
  • Employee Assistance Program and virtual care through Lumino Health

United Kingdom
  • Private medical insurance through Freedom Elite
  • Virtual GP and at-home care via eMed x Livi
  • Workplace pension through Penfold, with salary sacrifice option
  • Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay

Referral Instructions
If you are being referred for the role, please contact that person to apply on your behalf.
Other notices
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Beware of recruiting scams: Ramp will only contact you through official @Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.
Ramp Applicant Privacy Notice

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