You understand how incrementality testing, ranging from geo-based experiments to causal inference ... You leverage Artificial Intelligence and Machine Learning to elevate these frameworks to ...
You understand how incrementality testing, ranging from geo-based experiments to causal inference ... You leverage Artificial Intelligence and Machine Learning to elevate these frameworks to ...
Principal Data Scientist
Oakland, CA · On-site
$128 - $148/hr
... Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field. * Expertise in experimental design and causal inference methods.
Quick apply
Principal Data Scientist
Oakland, CA · On-site
$128 - $148/hr
... Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field. * Expertise in experimental design and causal inference methods.
Subject matter expertise in the realms of causal inference, machine learning, and experimental design * Sharp product sense and practical experience utilizing various causal methodologies * Technical ...
Subject matter expertise in the realms of causal inference, machine learning, and experimental design * Sharp product sense and practical experience utilizing various causal methodologies * Technical ...
You understand how incrementality testing, ranging from geo-based experiments to causal inference ... You leverage Artificial Intelligence and Machine Learning to elevate these frameworks to ...
You understand how incrementality testing, ranging from geo-based experiments to causal inference ... You leverage Artificial Intelligence and Machine Learning to elevate these frameworks to ...
Subject matter expertise in the realms of causal inference, machine learning, and experimental design * Sharp product sense and practical experience utilizing various causal methodologies * Technical ...
Subject matter expertise in the realms of causal inference, machine learning, and experimental design * Sharp product sense and practical experience utilizing various causal methodologies * Technical ...
Senior Data Scientist
$149K - $202K/yr
This role requires a foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity ...
Senior Data Scientist
$149K - $202K/yr
This role requires a foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity ...
Senior Data Scientist
Mountain View, CA · On-site
$149K - $202K/yr
This role requires a foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity ...
Senior Data Scientist
Mountain View, CA · On-site
$149K - $202K/yr
This role requires a foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity ...
Senior Data Scientist
San Diego, CA · On-site
$149K - $202K/yr
This role requires a foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity ...
Senior Data Scientist
San Diego, CA · On-site
$149K - $202K/yr
This role requires a foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity ...
Senior Data Scientist
$149K - $202K/yr
This role requires a foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity ...
Senior Data Scientist
$149K - $202K/yr
This role requires a foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity ...
Applied ML Science Manager
Cupertino, CA · On-site
... causal inference, statistics, and machine learning to help drive key business decisions and optimize marketing channels, via observational testing frameworks, counterfactual modeling, and lifetime ...
Applied ML Science Manager
Cupertino, CA · On-site
... causal inference, statistics, and machine learning to help drive key business decisions and optimize marketing channels, via observational testing frameworks, counterfactual modeling, and lifetime ...
Machine Learning Engineer, Next-Generation Recommendation Systems
Mountain View, CA · On-site +1
$127K - $191K/yr
Design and run rigorous experiments using causal inference, A/B testing, and offline evaluation ... PhD in Computer Science, Machine Learning, Statistics, or a related field (graduating 2026 or ...
Machine Learning Engineer, Next-Generation Recommendation Systems
Mountain View, CA · On-site +1
$127K - $191K/yr
Design and run rigorous experiments using causal inference, A/B testing, and offline evaluation ... PhD in Computer Science, Machine Learning, Statistics, or a related field (graduating 2026 or ...
Senior Data Scientist
San Diego, CA · On-site
$149K - $202K/yr
This role requires a foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity ...
Senior Data Scientist
San Diego, CA · On-site
$149K - $202K/yr
This role requires a foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity ...
Principal Data Scientist
Oakland, CA · On-site
... Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field. * Expertise in experimental design and causal inference methods.
Principal Data Scientist
Oakland, CA · On-site
... Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field. * Expertise in experimental design and causal inference methods.
Python Machine Learning, data science, AWS, Statistical Modeling, Semantic Search, Vector DB, GenAI ... Robust knowledge of causal inference approaches such as propensity scores, synthetic controls ...
Quick apply
Python Machine Learning, data science, AWS, Statistical Modeling, Semantic Search, Vector DB, GenAI ... Robust knowledge of causal inference approaches such as propensity scores, synthetic controls ...
Data Scientist
Glendale, CA · On-site
Must Have: Python Machine Learning, data science, AWS, Statistical Modeling, Semantic Search ... Advanced Statistical & Causal Inference: Apply deep knowledge of experimental design, regression ...
Quick apply
Data Scientist
Glendale, CA · On-site
Must Have: Python Machine Learning, data science, AWS, Statistical Modeling, Semantic Search ... Advanced Statistical & Causal Inference: Apply deep knowledge of experimental design, regression ...
Staff Data Scientist
$185K - $251K/yr
This role demands a strong foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ...
Staff Data Scientist
$185K - $251K/yr
This role demands a strong foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ...
Machine Learning Inference Engineer
San Francisco, CA · On-site
$200K - $250K/yr
Title: Machine Learning Inference Engineer Location: San Francisco, CA - Hybrid Salary: $200K-$250K + Equity A rapidly growing AI startup is hiring a Machine Learning Inference Engineer to help ...
Machine Learning Inference Engineer
San Francisco, CA · On-site
$200K - $250K/yr
Title: Machine Learning Inference Engineer Location: San Francisco, CA - Hybrid Salary: $200K-$250K + Equity A rapidly growing AI startup is hiring a Machine Learning Inference Engineer to help ...
Staff Data Scientist
$185K - $251K/yr
This role demands a strong foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ...
Staff Data Scientist
$185K - $251K/yr
This role demands a strong foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ...
Staff Data Scientist
San Diego, CA · On-site
$185K - $251K/yr
This role demands a strong foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ...
Staff Data Scientist
San Diego, CA · On-site
$185K - $251K/yr
This role demands a strong foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ...
Machine Learning - Research
San Francisco, CA · On-site
$241K/yr
Our mission is general causal intelligence, AI that is capable of (1) predicting the future and (2) ... Familiarity with distributed training and inference. * [bonus] Familiarity with meteorology ...
Machine Learning - Research
San Francisco, CA · On-site
$241K/yr
Our mission is general causal intelligence, AI that is capable of (1) predicting the future and (2) ... Familiarity with distributed training and inference. * [bonus] Familiarity with meteorology ...
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?
| Aspect | Causal Inference Machine Learning Postdoctoral | Data Scientist |
|---|---|---|
| Required Credentials | PhD in statistics, machine learning, or related field | Bachelor's or Master's in data science, computer science, or related field |
| Work Environment | Academic research, research labs, universities | Corporate, tech companies, startups |
| Industry Usage | Research, academia, specialized industry projects | Business analytics, product development, data-driven decision making |
| Common Search/Comparison | Yes | Yes |
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 California? For Causal Inference Machine Learning Postdoctoral jobs in California, the most frequently searched job titles are:
- Flexible Postdoctoral Medicinal Chemistry
- Postdoctoral Teaching Fellow
- Mathematical Programming Postdoc
- Postdoctoral Fellowship Physics
- Postdoctoral Position Microbiology Bacteria
- Remote Neuroscience Postdoc
- Phylogenetics
- Postdoctoral Position Microbiology
- Remote Postdoctoral Teaching Fellow
- Postdoctoral Materials Science
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in California look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in California are:
What cities in California are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities in California with the most Causal Inference Machine Learning Postdoctoral job openings:
Full-time
Re-posted 9 days ago
Apple rating
8.1
Based on 670 frontline employees who took The Breakroom Quiz
5th of 30 rated technology retailers
Job description
Apple is where individual imaginations gather, committing to values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. This happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation running through everything we do. When we bring everybody in, we can do the best work of our lives.
Here, you'll do more than join something; you'll add something. At Apple, extraordinary ideas have a way of becoming great products, services, and customer experiences very quickly.
Description
We are seeking an experienced Data Scientist with the intellectual curiosity and strategic depth to reimagine how Apple Pay measures and optimizes its marketing. You do not wait to be handed a question. Instead, you identify the questions worth asking, conceptualize the ideal frameworks to answer them, and propose innovative approaches that others have yet to consider. You possess a deep understanding of the marketing and media landscape. You know how marketing mix models quantify cross-channel effectiveness using statistical and econometric techniques. You understand
how incrementality testing, ranging from geo-based experiments to causal inference methods, isolates true causal lift.
Furthermore, you know how behavioral signals derived from clustering, propensity modeling, and sequence analysis can shape smarter audience strategies and campaign designs. What sets you apart is your ability to architect the right measurement framework before a single model is built. You excel at identifying the causal assumptions that must hold, the confounders that must be controlled, and the experimental conditions required to make results actionable.
You leverage Artificial Intelligence and Machine Learning to elevate these frameworks to unprecedented levels of rigor, scale, and speed. This includes building production-grade causal inference pipelines, designing ML-powered experiment analyses, and applying Large Language Models (LLMs) to accelerate how insights are generated and communicated.
Minimum Qualifications
Hands-on experience in marketing science, including building marketing mix models, causal inference, and incrementality measurement.
Proven experience designing and executing rigorous marketing experiments.
Demonstrated proficiency in applying ML techniques to large-scale marketing and customer datasets.
Strong programming skills in Python and data science libraries (such as pandas, NumPy, scikit-learn, and statsmodels).
Advanced command of SQL for querying, manipulating, and analyzing massive marketing and media datasets.
Familiarity with Generative AI and large language models, along with a comfort level in integrating AI tools into daily analytical workflows.
Exceptional written and verbal communication skills, with the ability to tell compelling stories with data to diverse technical and non-technical stakeholders.
Preferred Qualifications
Experience analyzing paid media data across various channels, including paid digital, in-store media, social, and other performance marketing platforms.
Deep understanding of both awareness and performance marketing measurement.
A track record of actively following industry trends in marketing science and media measurement, with a habit of bringing emerging methodologies and tools to the team.
Experience applying Generative AI directly to marketing workflows, such as budget optimization, automated creative analysis, or campaign performance reporting.
Advanced degree (M.S. or Ph.D.) in Statistics, Machine Learning, Econometrics, Marketing Science, or a related quantitative field.
Here, you'll do more than join something; you'll add something. At Apple, extraordinary ideas have a way of becoming great products, services, and customer experiences very quickly.
Description
We are seeking an experienced Data Scientist with the intellectual curiosity and strategic depth to reimagine how Apple Pay measures and optimizes its marketing. You do not wait to be handed a question. Instead, you identify the questions worth asking, conceptualize the ideal frameworks to answer them, and propose innovative approaches that others have yet to consider. You possess a deep understanding of the marketing and media landscape. You know how marketing mix models quantify cross-channel effectiveness using statistical and econometric techniques. You understand
how incrementality testing, ranging from geo-based experiments to causal inference methods, isolates true causal lift.
Furthermore, you know how behavioral signals derived from clustering, propensity modeling, and sequence analysis can shape smarter audience strategies and campaign designs. What sets you apart is your ability to architect the right measurement framework before a single model is built. You excel at identifying the causal assumptions that must hold, the confounders that must be controlled, and the experimental conditions required to make results actionable.
You leverage Artificial Intelligence and Machine Learning to elevate these frameworks to unprecedented levels of rigor, scale, and speed. This includes building production-grade causal inference pipelines, designing ML-powered experiment analyses, and applying Large Language Models (LLMs) to accelerate how insights are generated and communicated.
Minimum Qualifications
Hands-on experience in marketing science, including building marketing mix models, causal inference, and incrementality measurement.
Proven experience designing and executing rigorous marketing experiments.
Demonstrated proficiency in applying ML techniques to large-scale marketing and customer datasets.
Strong programming skills in Python and data science libraries (such as pandas, NumPy, scikit-learn, and statsmodels).
Advanced command of SQL for querying, manipulating, and analyzing massive marketing and media datasets.
Familiarity with Generative AI and large language models, along with a comfort level in integrating AI tools into daily analytical workflows.
Exceptional written and verbal communication skills, with the ability to tell compelling stories with data to diverse technical and non-technical stakeholders.
Preferred Qualifications
Experience analyzing paid media data across various channels, including paid digital, in-store media, social, and other performance marketing platforms.
Deep understanding of both awareness and performance marketing measurement.
A track record of actively following industry trends in marketing science and media measurement, with a habit of bringing emerging methodologies and tools to the team.
Experience applying Generative AI directly to marketing workflows, such as budget optimization, automated creative analysis, or campaign performance reporting.
Advanced degree (M.S. or Ph.D.) in Statistics, Machine Learning, Econometrics, Marketing Science, or a related quantitative field.
About Apple
Sourced by ZipRecruiter
Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.
Industry
Computer and electronic product manufacturing
Company size
10,000+ Employees
Headquarters location
Cupertino, CA, US
Year founded
1976