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Machine Learning Computational Chemistry Jobs in Missouri

Saint Louis University

Post Doctoral Fellow

Saint Louis, MO · On-site

$47K - $64K/yr

Develop and apply statistical and machine-learning models (e.g., mixed-effects models, survival ... PhD in Computational Biology, Bioinformatics, Biostatistics, Data Science, Systems Biology, or a ...

Jobgether

$76K - $96K/yr

... the computational cost of training and inference. The role combines fundamental research with ... PhD in machine learning or a closely related field, or equivalent depth of research experience.

Varsity Tutors

Linear Algebra Tutor

Saint Louis, MO · Remote

$18 - $40/hr

... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction ... Adapts instruction using matrix visualization tools, computational software like MATLAB or Python ...

Varsity Tutors

Linear Algebra Tutor

Kansas City, MO · Remote

$18 - $40/hr

... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction ... Adapts instruction using matrix visualization tools, computational software like MATLAB or Python ...

Varsity Tutors

Linear Algebra Tutor

Columbia, MO · Remote

$18 - $40/hr

... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction ... Adapts instruction using matrix visualization tools, computational software like MATLAB or Python ...

TekWissen LLC

IT Data Engineer (Entry)

Creve Coeur, MO · On-site

$58/hr

... and computational infrastructure is available to support rapid development and testing on machine and deep learning models that create value for our farmers * Collaborate across the Science ...

Walmart

Group Director, Data Stewardship

Cassville, MO · On-site

$130K - $260K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Advanced degree in Computer Science, Data Science, Statistics, Mathematics, Information Science, Computational Linguistics, AI, Machine Learning, or related fields.  * Experience in retail, e ...

Walmart

Group Director, Data Stewardship

Noel, MO · On-site

$130K - $260K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Advanced degree in Computer Science, Data Science, Statistics, Mathematics, Information Science, Computational Linguistics, AI, Machine Learning, or related fields.  * Experience in retail, e ...

Walmart

Group Director, Data Stewardship

Anderson, MO · On-site

$130K - $260K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Advanced degree in Computer Science, Data Science, Statistics, Mathematics, Information Science, Computational Linguistics, AI, Machine Learning, or related fields.  * Experience in retail, e ...

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Showing results 41-60

All JobsMachine Learning Computational Chemistry JobsMachine Learning Computational Chemistry Jobs in Missouri

Machine Learning Computational Chemistry information

See Missouri salary details

$21.3K

$99.7K

$184.2K

How much do machine learning computational chemistry jobs pay per year?

As of Aug 16, 2026, the average yearly pay for machine learning computational chemistry in Missouri is $99,688.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,057.00 and $134,550.00 per year, depending on experience, location, and employer.

Is computational chemistry in demand?

Computational chemistry is in high demand within industries such as pharmaceuticals, materials science, and chemical research, where it supports drug discovery and molecular modeling. Professionals with skills in machine learning, programming, and chemistry are increasingly sought after to develop advanced simulation tools and analyze complex data sets.

What is the difference between Machine Learning Computational Chemistry vs Computational Chemist?

AspectMachine Learning Computational ChemistryComputational Chemist
Required CredentialsAdvanced degrees in chemistry, computer science, or related fields; knowledge of machine learning and programmingDegree in chemistry, chemical engineering, or related fields; strong background in chemical theory and modeling
Work EnvironmentResearch labs, tech companies, academia; focus on algorithm development and data analysisLaboratories, research institutions, industry; focus on chemical modeling and simulation
Employer & Industry UsageTech firms, pharmaceutical companies, research institutions applying AI/ML techniquesPharmaceutical, chemical, and materials industries conducting chemical research and development

Machine Learning Computational Chemists specialize in applying machine learning algorithms to chemical data, enhancing predictive models and simulations. Computational Chemists focus on traditional chemical modeling and simulations using computational methods. Both roles require strong chemistry backgrounds, but Machine Learning Computational Chemists emphasize data science and AI skills, while Computational Chemists focus on chemical theory and modeling techniques.

What is machine learning computational chemistry?

Machine learning computational chemistry is a field that combines machine learning techniques with computational chemistry to accelerate the discovery and design of molecules and materials. By training algorithms on large datasets of chemical information, researchers can predict molecular properties, simulate chemical reactions, and optimize compounds more efficiently than traditional methods. This approach helps reduce the time and cost required for research in drug discovery, materials science, and related fields.

What are some common challenges faced by professionals working in machine learning computational chemistry roles?

One common challenge in Machine Learning Computational Chemistry roles is integrating large and often complex chemical datasets with appropriate machine learning models, which requires a solid understanding of both domains. Professionals may also encounter difficulties in ensuring that their models are both interpretable and generalizable to new data, as overfitting is a frequent issue. Additionally, collaboration with chemists and data scientists is essential, so clear communication across disciplines is key to success. Staying up to date with the latest developments in both computational chemistry and machine learning is crucial for ongoing professional growth.

What are the key skills and qualifications needed to thrive as a machine learning computational chemist, and why are they important?

To thrive as a Machine Learning Computational Chemist, you need a solid background in chemistry, mathematics, and computer science, typically supported by an advanced degree in computational chemistry, cheminformatics, or a related field. Proficiency with programming languages (such as Python), machine learning frameworks (like TensorFlow or PyTorch), and molecular modeling software is essential. Strong analytical thinking, problem-solving skills, and effective collaboration are key soft skills that help drive innovation and teamwork. These skills and qualifications are critical for developing accurate models, advancing research, and translating computational insights into real-world chemical solutions.

What are popular job titles related to Machine Learning Computational Chemistry jobs in Missouri?

For Machine Learning Computational Chemistry jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Machine Learning Computational Chemistry jobs in Missouri look for?

The top searched job categories for Machine Learning Computational Chemistry jobs in Missouri are:

What cities in Missouri are hiring for Machine Learning Computational Chemistry jobs?

Cities in Missouri with the most Machine Learning Computational Chemistry job openings:

Machine Learning Computational Chemistry jobs near you

Applied Data Scientist - Vehicle Prognostics

Jobtailor

Dearborn, MO • On-site

$110 - $160/hr

Other

Posted 11 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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