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Causal Inference Phd Internship Jobs in Missouri

Socket.dev

Data Scientist

California, MO · On-site

$120 - $180/hr

Apply causal inference techniques (e.g., difference-in-differences, propensity score matching ... Advanced degree (MS/PhD) in Statistics, Economics, Computer Science, or a quantitative field

Walmart

Staff, Data Scientist (Pricing)

Noel, MO · On-site

$110K - $220K/yr

Causal Inference & Elasticity: Identification of treatment effects beyond simple log-log approaches ... What You'll Bring: * 8+ years in Data Science / Applied ML (or PhD + 5 years), with deep hands-on ...

Walmart

Staff, Data Scientist (Pricing)

Cassville, MO · On-site

$110K - $220K/yr

Causal Inference & Elasticity: Identification of treatment effects beyond simple log-log approaches ... What You'll Bring: * 8+ years in Data Science / Applied ML (or PhD + 5 years), with deep hands-on ...

Walmart

Staff, Data Scientist (Pricing)

Anderson, MO · On-site

$110K - $220K/yr

Causal Inference & Elasticity: Identification of treatment effects beyond simple log-log approaches ... What You'll Bring: * 8+ years in Data Science / Applied ML (or PhD + 5 years), with deep hands-on ...

Walmart

(USA) Senior Manager, Data Science

Anderson, MO · On-site

$110K - $220K/yr

Causal Inference: Ability to apply causal frameworks to observational data. * Forecasting & Anomaly ... Advanced degree (MS/PhD) in a quantitative field (CS, Stats, OR, Econ) is preferred. Why This Role?

Walmart

(USA) Senior Manager, Data Science

Cassville, MO · On-site

$110K - $220K/yr

Causal Inference: Ability to apply causal frameworks to observational data. * Forecasting & Anomaly ... Advanced degree (MS/PhD) in a quantitative field (CS, Stats, OR, Econ) is preferred. Why This Role?

Walmart

(USA) Senior Manager, Data Science

Noel, MO · On-site

$110K - $220K/yr

Causal Inference: Ability to apply causal frameworks to observational data. * Forecasting & Anomaly ... Advanced degree (MS/PhD) in a quantitative field (CS, Stats, OR, Econ) is preferred. Why This Role?

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Causal Inference Phd Internship information

What types of projects does a causal inference PhD intern typically work on during their internship?

Causal Inference PhD interns often engage in projects that involve designing and analyzing experiments or observational studies to draw valid conclusions about cause-and-effect relationships. These projects might include developing statistical models, collaborating with data scientists and product teams, and presenting findings to inform business or policy decisions. Interns usually have the opportunity to work with large-scale, real-world data, and are encouraged to publish or present their work at conferences, supporting both professional growth and academic development.

What are the key skills and qualifications needed to thrive as a causal inference PhD intern, and why are they important?

To thrive as a Causal Inference PhD Intern, you need a strong background in statistics, econometrics, and causal inference methods, often supported by advanced graduate studies in a related field. Familiarity with statistical programming languages such as R or Python, and experience using data analysis tools and frameworks like Stata or TensorFlow Probability, are typically required. Excellent problem-solving abilities, critical thinking, and the ability to communicate complex concepts clearly help you stand out in this role. These skills and qualities are crucial for designing robust experiments, drawing reliable conclusions, and effectively collaborating with interdisciplinary research teams.

What is the difference between Causal Inference Phd Internship vs Data Scientist Internship?

AspectCausal Inference Phd InternshipData Scientist Internship
Required CredentialsPhD in statistics, economics, or related fieldBachelor's or Master's in CS, statistics, or related field
Work EnvironmentResearch-focused, academic or industry research teamsData analysis, modeling, and business insights
Employer & Industry UsageResearch institutions, tech companies, financeTech firms, startups, finance, healthcare
Search & Comparison IntentFocus on causal inference research rolesBroader data analysis roles

While a Causal Inference Phd Internship emphasizes research in causal analysis with advanced credentials, a Data Scientist Internship covers broader data analysis skills suitable for various industries. Both roles involve working with data, but their focus, required background, and career paths differ significantly.

What is a causal inference PhD internship?

A Causal Inference PhD Internship is a specialized research position for doctoral students focused on causal inference, which involves determining cause-and-effect relationships from data. Interns typically work with large datasets, advanced statistical models, and machine learning techniques to answer questions about how variables influence one another. These internships are often offered by tech companies, research labs, or policy organizations and provide hands-on experience in designing experiments, analyzing observational data, and developing new methodologies. The goal is to bridge academic research with real-world applications, contributing to projects that require rigorous causal analysis.
What are popular job titles related to Causal Inference Phd Internship jobs in Missouri? For Causal Inference Phd Internship jobs in Missouri, the most frequently searched job titles are:
What job categories do people searching Causal Inference Phd Internship jobs in Missouri look for? The top searched job categories for Causal Inference Phd Internship jobs in Missouri are:
What cities in Missouri are hiring for Causal Inference Phd Internship jobs? Cities in Missouri with the most Causal Inference Phd Internship job openings:
Causal Inference Phd Internship jobs near you

Applied Data Scientist - Vehicle Prognostics

Jobtailor

Dearborn, MO • On-site

$110 - $160/hr

Other

Posted 5 days ago

Job description

Responsibilities
  • Own the process for prognostic feature development from conceptual to feature deployment to our production vehicles.
  • Pioneer Physics-Informed Machine Learning (PIML): Fuse first-principles physics modeling with advanced machine learning to develop hybrid, high-fidelity prognostic models that capture complex degradation behaviors across both EV and ICE powertrains.
  • Architect Prognostics & RUL Frameworks: Design and deploy state-of-the-art prognostics models to accurately estimate the Remaining Useful Life (RUL) of critical vehicle subsystems, transforming noisy fleet data into actionable maintenance alerts.
  • Deploy Edge Models in C++: Translate complex predictive models into highly optimized, low-latency C++ code, bridging the gap between cloud-based data science and resource-constrained on-board vehicle electronic control units (ECUs).
  • Harness High-Frequency Signal Processing: Architect custom Digital Signal Processing (DSP) pipelines and time-series analytics to extract clean, high-frequency physical signatures from multi-sensor vehicle networks, isolating early-stage wear patterns before they manifest as failures.
  • Design Multi-Sensor Fault Detection & Isolation (FDI): Develop and validate intelligent, multi-sensor anomaly detection frameworks capable of real-time Fault Detection and Isolation (FDI) to ensure vehicle safety, system redundancy, and fault-tolerant control.
  • Apply Statistical Causal Inference: Leverage advanced statistical methods (including causal inference, multivariate analysis, ANOVA, and PCA) to differentiate between mere correlation and true physical root causes of component degradation across massive, connected vehicle fleets.
  • Own the End-to-End Pipeline (HIL to Production): Direct the entire prognostic lifecycle—moving seamlessly from mathematical conceptualization and simulation in MATLAB/Simulink to physical validation on Hardware-in-the-Loop (HIL) benches, prototype vehicles, and ultimately to production vehicle deployment.
  • Synthesize Deep Subsystem Domain Knowledge: Partner closely with EV and ICE component subject matter experts to translate deep physical domain knowledge (thermal, mechanical, chemical, and electrical) into robust on-board and off-board diagnostics.
  • Build Scale with Big Data & Calibration Tools: Ingest and process large-scale telemetry data using Python, SQL, Spark, and Hadoop, while leveraging industry-standard calibration tools (such as ATI and ETAS) to fine-tune algorithms for real-world driving environments. Interact with subject matter experts to understand component/system functions, leverage existing connected vehicle data to model on-board and off-board prognostics algorithms.
  • Operate cross-functionally to ensure successful code implementation on production vehicles.
Requirements
  • Bachelor's in Mechanical, Electrical, Computer Science, Computer engineering, Physics, Mathematics or related fields or a combination of education and equivalent experience
  • 4+ years of experience of practicing statistical methods and their accurate application e.g. ANOVA, principal component analysis, correspondence analysis, k-means clustering, factor analysis, multi-variate analysis, Neural Networks, causal inference, Gaussian regression, etc.
  • 3+ Experience with Python (and related modules), SQL
  • Experience with embedded controls, onboard Diagnostic, Sensor Processing, General First Principles Physics Modeling and simulation using numerical computational tool (e.g. MATLAB, ATI, Simulink)
  • Experience with Digital Signal Processing (DSP) data structures, algorithms, and software engineering principles
  • Self-motivated, strong analytical, excellent interpersonal and communication skills required
  • Even better, you may have...
  • Master's or PhD in Mechanical, Electrical, Computer Science, Computer engineering, Physics, Mathematics or related fields or a combination of education and equivalent experience
  • Experience in Dynamic Systems, Control, Robotics, Prognostics and Health Management
  • Familiarity working with Automotive prognostics feature development using connected vehicle data.
  • 2+ Experience in application of statistical and machine learning methods e.g., ANOVA, PCA, clustering methods, causal inference, time series forecasting, random forest, multi-variate analysis, neural networks, etc.
  • Expertise in open-source data science technologies such as Python, R, Spark, Hadoop, etc. acquired through college course work, online training and certification or project development.
  • Experience in software development for automotive controls with hands on experience using MATLAB for large scale data and understanding of programming fundamentals and experience with C++ programming in embedded environments. ATI and ETAS calibration tool familiarity
  • Excellent verbal and written skills. Highly credible in organizational, time management, decision making and problem-solving skills.
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