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Causal Inference Machine Learning Postdoctoral Jobs in New York

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 ...

4 Staffing Corp

Experienced Data Scientist

Manhattan, NY · Hybrid

... causal inference to join their team. The role involves developing advanced data science solutions using machine learning and artificial intelligence to drive innovation across various business areas ...

4 Staffing Corp

Experienced Data Scientist

Manhattan, NY · Hybrid

... causal inference to join their team. The role involves developing advanced data science solutions using machine learning and artificial intelligence to drive innovation across various business areas ...

Colgate-Palmolive

Machine Learning Engineer

Manhattan, NY · On-site

$105 - $150/hr

Overview We are seeking a Machine Learning Engineer who brings the analytical rigor of a data ... Proficient with a selection of Bayesian methods, causal inference, and/or predictive modeling ...

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?

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.

Is it difficult to get a causal inference machine learning postdoctoral position?

Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.

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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:

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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

Staff Machine Learning Engineer

The Estée Lauder Companies, Inc.

New York, NY • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 20 days ago

Job description

The Estée Lauder Companies Inc. is one of the world's leading manufacturers, marketers, and sellers of quality skin care, makeup, fragrance, and hair care products, and is a steward of luxury and prestige brands globally. The company's products are sold in approximately 150 countries and territories under brand names including: Estée Lauder, Aramis, Clinique, Lab Series, Origins, M• A• C, La Mer, Bobbi Brown Cosmetics, Aveda, Jo Malone London, Bumble and bumble, Darphin Paris, TOM FORD, Smashbox, AERIN Beauty, Le Labo, Editions de Parfums Frédéric Malle, GLAMGLOW, KILIAN PARIS, Too Faced, Dr.Jart+, the DECIEM family of brands, including The Ordinary and NIOD, and BALMAIN Beauty.
Description
As a Staff Machine Learning Engineer, you're a player-coach: a strong hands-on contributor who also sets standards, reviews others' work, authors the designs others build against, and mentors the team. You design, build, test, and deploy production AI products and the platform beneath them, and you shape how we build and integrate AI systems - working with engineers, scientists, product managers, and domain experts to turn ambiguous problems into reliable, scalable deliverables.
The core of this role is engineering: agentic AI products and their platform, including stateful, transactional services where reliability and data-modeling rigor matter. Classical ML, statistics, and experimentation are valued and let us take on predictive and inference work when the roadmap calls for it - but you may spend long stretches purely on AI-product and platform engineering, so we're looking for someone energized by that work.
Responsibilities
• Design, build, test, and deploy production agentic / LLM-powered products end-to-end on Google Cloud, along with the shared platform, tooling, and harnesses beneath them.
• Bring strong software-engineering rigor to our services - including stateful and transactional (OLTP) systems - across data modeling, concurrency, idempotency, reliability, observability, and testing.
• Apply classical ML, statistics, and experimentation where the problem calls for it (predictive modeling, statistical inference, hypothesis testing, causal / incrementality measurement).
• Build scalable tools that drive automation across domains such as recommendation and personalization, conversational and customer experiences, and measurement and incrementality.
• Author design documents, uphold standards for clean code and documentation, and review others' designs and deliverables.
• Mentor and grow engineers and scientists, and support our early-career and student mentorship efforts.
• Partner with engineering, product, and domain-expert teams on major cross-functional automation, measurement, and modeling efforts.
• At least 80% technical contributor, up to 20% leadership / management.
• Actively participate in ELC's diversity and inclusion agenda.
Qualifications
• BS/BA in a quantitative or technical field (e.g., Computer Science, Statistics, Mathematics, Physics, Engineering, Operations Research, Economics), or equivalent practical experience; a graduate degree is a plus.
• 5+ years of experience (3+ with a graduate degree) across Software Engineering, Machine Learning, and/or Data Science.
• Strong general software engineering in Python - clean API and service design, data-structure and algorithm proficiency, and real testing discipline - with experience building and operating production-grade services, including stateful / transactional systems and databases (SQL and/or NoSQL).
• Experience building and deploying LLM-powered or agentic systems in production (e.g., retrieval-augmented generation, agents and tool use, structured outputs, evaluation, safety) - or a strong production-ML background paired with a clear appetite to work in this space.
• Experience deploying and operating systems on a major cloud platform (Google Cloud a plus) with sound data-processing practices at scale.
• Uses agentic / AI-assisted development tools, or is eager to adopt them in earnest, with judgment about where they help and how to keep quality high.
• A track record of mentorship, a continuous-learning mindset, a bias for simplicity, and a collaborative, shared-ownership working style.
Preferred / a strong plus
• Genuine classical ML / data science / statistics depth - statistical modeling, hypothesis testing and experimentation, causal inference and incrementality, and predictive modeling. We value this as a way to de-risk future modeling work; it is hard to acquire on the job, so we welcome it even when the immediate work is engineering-focused.
• Hands-on experience in NLP, agent frameworks, or advanced statistical methods.
• Familiarity with our stack: Google Cloud (Vertex AI, Cloud Run, BigQuery, Firestore, Pub/Sub), async Python (FastAPI), container-based CI/CD, and the Model Context Protocol (MCP).
• Experience designing a technical roadmap and leading execution in a business environment.
Pay Range:
The anticipated base salary range for this position is $119,300.00 to $196,600.00. Exact salary depends on several factors such as experience, skills, education, and budget. Salary range may vary based on geographic location. In addition to base salary, this position is eligible for participation in a highly competitive bonus program as well as participation in the share incentive plan. In addition,
In addition to base salary, this position is eligible for participation in a highly competitive bonus program with the possibility for overachievement based on performance and company results. In addition, The Estée Lauder Companies offers a variety of benefits to eligible employees, including health insurance coverage (medical, dental, and vision insurance), wellness and family support programs, life and disability insurance, retirement savings plans, paid leave programs, education-related programs, paid holidays and vacation time, and many others. Many of these benefits are subsidized or fully paid for by the company.
Equal Opportunity Employer
It is Company's policy not to discriminate against any employee or applicant for employment on the basis of race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth and related medical conditions), gender identity or gender expression (including transgender status), sexual orientation, marital status, military service and veteran status, physical or mental disability, protected medical condition as defined by applicable state or local law, genetic information, or any other characteristic protected by applicable federal, state, or local laws and ordinances. The Company will endeavor to provide a reasonable accommodation consistent with the law to otherwise qualified employees and prospective employees with a disability and to employees and prospective employees with needs related to their religious observance or practices. Should you wish to apply for this position or any other position with the Company and you believe you require assistance to complete an application or participate in an interview, please contact USApplicantAccommodations@Estee.com.
Michigan Applicants: Persons with disabilities needing accommodations for employment must notify the company in writing of the need for an accommodation within 182 days after the date the person with a disability knew or reasonably should have known that an accommodation was needed.
Philadelphia Applicants: Philadelphia's Fair Chance Hiring Law
Rhode Island Applicants: The company is subject to chapters 29-38 of title 28 of the general laws of Rhode Island and is therefore covered by the state's workers' compensation law.

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