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Causal Inference Machine Learning Postdoctoral Jobs in New York, NY

Walmart

(USA) Principal, Data Scientist

Hoboken, NJ · On-site

$132K - $264K/yr

This team leads advancements in generative AI, agentic intelligence, machine learning, measurement, and causal inference to redefine retail experiences, optimize operations, and develop new business ...

MasterCard

Senior Data Scientist

New York, NY · On-site

Design, build, and deploy machine learning models for ad targeting, ranking, and bidding ... Strong background in incrementality measurement, experimentation, A/B testing, causal inference ...

OpenX

Data Scientist III

New York, NY · On-site

Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design ...

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Showing results 21-40

All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in New York, NY

Causal Inference Machine Learning Postdoctoral information

See New York, NY salary details

$38.8K

$59.3K

$66.7K

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

As of Aug 9, 2026, the average yearly pay for causal inference machine learning postdoctoral in New York, NY is $59,322.00, according to ZipRecruiter salary data. Most workers in this role earn between $58,500.00 and $61,800.00 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.

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

Data Scientist (Senior/Staff) - VC Backed Startups

SignalFire

New York, NY • On-site

Full-time

Posted 4 days ago

Job description

Join SignalFire's Talent Network for Senior/Staff Data Scientist Roles at VC-Backed Startups
This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring Data Science talent. If you have any questions, please direct inquiries to talentnetwork@signalfire.com.
At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.
We're looking to connect with exceptional Senior and Staff Data Scientists who are excited about using data, experimentation, and machine learning to solve complex product and business problems at high-growth startups.
By joining SignalFire's Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.
Who Should Join?
We're looking for data scientists who are:
✔ Passionate about using data to improve products, customer outcomes, and business decisions
✔ Experienced in experimentation, statistical analysis, predictive modeling, or causal inference
✔ Excited to work closely with product, engineering, operations, and business teams
✔ Comfortable operating with incomplete data and ambiguous problems in fast-moving startup environments
✔ Interested in building scalable analytical frameworks, models, and decision-making systems
Typical Roles & Responsibilities
  • Partner with product, engineering, and business leaders to identify high-impact opportunities for data science
  • Design and analyze experiments to evaluate product changes, growth initiatives, and operational strategies
  • Develop predictive, forecasting, recommendation, ranking, or optimization models
  • Apply statistical methods and causal inference techniques to measure impact and inform decisions
  • Build metrics, analytical frameworks, and dashboards that improve visibility into product and business performance
  • Translate complex analyses into clear recommendations for technical and non-technical stakeholders
  • Collaborate with engineers to productionize models and integrate data science into customer-facing products
  • Identify patterns in user, customer, operational, and market data
  • Establish best practices for experimentation, model evaluation, data quality, and analytical rigor
  • Mentor other data scientists and raise the technical standard of the broader data organization
  • Help shape the company's data strategy, tooling, and long-term analytical roadmap

Common Qualifications
While each startup has its own hiring criteria, many Senior and Staff Data Scientist roles in our network look for:
  • 5+ years of experience in data science, applied statistics, machine learning, decision science, or a related field
  • Strong proficiency in Python, R, SQL, or similar analytical languages
  • Experience with statistical modeling, experimentation, causal inference, forecasting, or predictive analytics
  • Track record of using data to influence product strategy, customer outcomes, or business performance
  • Ability to work with large, complex, and imperfect datasets
  • Experience partnering closely with product managers, engineers, operators, and executive stakeholders
  • Strong communication skills and the ability to explain technical findings clearly
  • Experience developing models or analytical systems that are used in production or operational decision-making
  • Strong judgment around methodology, measurement, tradeoffs, and uncertainty
  • Experience in venture-backed startups or rapidly scaling technology companies may be preferred
  • Advanced degree in statistics, economics, computer science, mathematics, operations research, or a related field may be preferred, but is not always required

Technologies You Might Work With:
  • Languages & Analysis: Python, R, SQL, pandas, NumPy, SciPy
  • Modeling & Machine Learning: scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow
  • Experimentation & Statistics: A/B testing, causal inference, Bayesian methods, time-series analysis
  • Data Platforms: Snowflake, BigQuery, Redshift, Databricks, Spark
  • Visualization & Analytics: Looker, Tableau, Mode, Hex, Amplitude
  • Workflow & Development: Jupyter, dbt, Airflow, Git, Docker, cloud platforms

What Happens Next?
  1. Submit your application to join SignalFire's Talent Ecosystem.
  2. We review applications on an ongoing basis to identify strong candidates.
  3. If there's a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
  4. No match yet? We'll keep your profile on file for future Senior and Staff Data Scientist roles across our portfolio.

Apply