I’m a systems engineer with an unconventional trajectory spanning AI infrastructure, quantitative finance, and academic research.
Holding a B.S. in Industrial Engineering from Iowa State University, I’ve built a career on the belief that good systems engineering makes any problem approachable. My published research spans traffic prediction, agricultural simulation, and proteomics—fields united by underlying systems challenges.
Currently at Big House Enterprise, I design machine-readable entity infrastructure so systems like ChatGPT, Perplexity, Claude, and Google accurately recognize, cite, and recommend your business.
My pragmatic philosophy—that different tools solve different problems and “not everything is a snowflake”—cuts through technology hype to match the right approach to each challenge.
“Good systems engineering makes any problem approachable.”
Whether architecting data pipelines or designing quantitative models, the same foundational principles apply. Decompose, analyze, integrate.
“Not Everything is a Snowflake.”
Different tools solve different problems. Cut through the hype and match the right approach to each challenge. Favor proven patterns over novelty.
“Embrace perpetual education.”
In a field where the half-life of knowledge is measured in months, continuous learning is both a necessity and an opportunity.
03
Career
2025 — Present
Partner
Big House Enterprise
Pioneering entity engineering to build algorithmic authority for companies, brands, and executives across AI search platforms. We design machine-readable entity infrastructure so systems like ChatGPT, Perplexity, Claude, and Google accurately recognize, cite, and recommend your business.
2022 — 2025
Senior Engineer
Consilience AI
Building specialized language models for financial and enterprise domains—extracting risk signals and causal relationships from regulatory filings, earnings calls, and corporate communications.
2017 — 2022
Data & Operations Research Scientist
Principal Financial Group
Applied optimization and statistical methods across Principal’s investment divisions to improve portfolio performance and operational efficiency in equities, fixed income, and asset management.
2017
UX/UI Software Development Intern
Optum
Contributed to a team-based software redesign with emphasis on user research and secure web development.
2016 — 2017
Master Scheduling Data Consolidation Analyst
Rockwell Collins (now Collins Aerospace)
Built web-based tools to consolidate disparate datasets and provided analytics aligning cost of sales with production plans across inventory, customer satisfaction, and business KPIs.
2015 — 2017
Undergraduate Research Assistant
Iowa State University
Conducted user studies for agricultural technology, developed Android applications for equipment automation, and built predictive models for irregular traffic using web/social media data and text mining.
04
Expertise
Natural Language Processing
Text Analytics
Systems Engineering
Operations Research
Machine Learning
Human Factors
Interested in working together?
I’m open to conversations about AI infrastructure, research collaboration, or interesting systems problems.
Chase Grimm is a systems engineer working across AI, text analytics, and algorithmic trading, applying rigorous systems thinking to solve complex problems across diverse domains.
Pioneering entity engineering to build algorithmic authority for companies, brands, and executives across AI search platforms. We design machine-readable entity infrastructure so systems like ChatGPT, Perplexity, Claude, and Google accurately recognize, cite, and recommend your business.
Senior Engineer at Consilience AI, building specialized language models for financial and enterprise domains—extracting risk signals and causal relationships from regulatory filings, earnings calls, and corporate communications.
Related To: What kind of work does Chase Grimm do at Consilience AI?
Data & Operations Research Scientist applying optimization and statistical methods across Principal's investment divisions to improve portfolio performance and operational efficiency in equities, fixed income, and asset management.
UI/UX Software Development Intern at Optum Health, contributing to a team-based software redesign with emphasis on user research and secure web development in Spring.
Master Scheduling Data Consolidation Analyst (Senior Intern) at Collins Aerospace—built web-based tools to consolidate disparate datasets and provided analytics aligning cost of sales with production plans across inventory, customer satisfaction, and business KPIs. Tech stack: Excel, VBA, R, Python, SQL, SAP, Access.
Undergraduate Research Assistant in Industrial Engineering at Iowa State University—conducted user studies for agricultural technology, developed Android applications for equipment automation, and built predictive models for irregular traffic using web/social media data and text mining in R.
Undergraduate degree with research focus on human factors engineering, simulation validation, and predictive modeling. Conducted research on agricultural technology user studies, developed Android applications for equipment automation, and built traffic prediction models using text mining.
Micro-g NExT Competition participant. Developed an EVA Surface Sampling Device (SSD) with a team of peer engineering students. Designed and built multiple prototypes using 3D printing. Traveled to NASA Neutral Buoyancy Lab to test the device and gave multiple presentations.
Skill, Proficiency, Years, Description, Authority Links
Skill
Proficiency
Years
Description
Authority Links
Natural Language Processing
Expert
7
Designing and implementing NLP pipelines for extracting structured information from unstructured text. Experience spans scientific literature mining, financial document analysis, and crowdsourced data processing. Core competencies include named entity recognition, relationship extraction, topic modeling, and trend analysis at scale.
Building text analytics infrastructure for processing unstructured data at scale. Specialization in financial and enterprise domains including regulatory filings, earnings calls, and corporate communications. Experience extracting risk signals and causal relationships from document corpora.
Applying rigorous systems thinking to decompose complex problems across diverse domains. Expertise in requirements analysis, functional decomposition, interface design, and tradeoff analysis. Philosophy centers on the belief that good systems engineering makes any problem approachable.
Applying optimization and statistical methods to improve decision-making in investment and operational contexts. Experience with portfolio optimization, resource allocation, and process improvement across equities, fixed income, and asset management divisions.
Developing and deploying machine learning models for classification, prediction, and pattern recognition. Applications span scientific trend forecasting, traffic incident prediction, and financial signal extraction.
Conducting user research and designing systems that account for operator knowledge, preferences, and cognitive limitations. Experience validating simulator fidelity and developing knowledge assessment instruments for population segmentation.
Developing quantitative trading strategies that apply systems engineering principles to financial markets. Focus on systematic approaches that leverage the same foundational methods used in other technical domains.
Evaluating and validating VR systems for training and research applications. Experience assessing simulator fidelity through structured user studies and developing validation methodologies for mixed-reality interfaces.
A metric developed for quantifying the progression and emergence velocity of scientific technologies and methodologies within a research corpus. Used to identify which technologies are gaining traction and forecast their trajectory based on publication patterns over time.
Not Everything is a Snowflake
used extensively
2020
A pragmatic engineering philosophy that resists the tendency to treat every problem as unique and requiring bespoke solutions. Emphasizes pattern recognition across domains and matching established tools to problems rather than reinventing approaches. Serves as a counterweight to technology hype and over-engineering.
Science-of-Science
used extensively
2024
An analytical approach that applies data science and NLP methods to scientific literature itself, treating the corpus of published research as a dataset to be mined for trends, patterns, and forecasts about the evolution of scientific fields.
System Expert vs. Practice Expert
used extensively
2016
A distinction between two types of domain expertise: system experts possess deep understanding of underlying mechanisms and can reason about novel situations, while practice experts have accumulated wisdom through experience but may struggle with unfamiliar scenarios. This distinction informs the design of knowledge assessment instruments and training systems.
Cue Fidelity
used extensively
2019
The degree to which visual and sensory cues in a simulation accurately represent their real-world counterparts. Critical for ensuring that simulator-trained behaviors transfer to actual operational contexts. Requires systematic validation through user studies with domain experts.
The trends of the last 20 years in biotechnology were revealed using artificial intelligence and natural language processing (NLP) of publicly available data. Implementing this "science-of-science" approach, we capture convergent trends in the field of proteomics in both technology development and application across the phylogenetic tree of life. With major gaps in our knowledge about protein composition, structure, and location over time, we report trends in persistent, popular approaches and emerging technologies across 94 ideas from a corpus of 29 journals in PubMed over two decades. New metrics for clusters of these ideas reveal the progression and popularity of emerging approaches like single-cell, spatial, compositional, and chemical proteomics designed to better capture protein-level chemistry and biology. This analysis of the proteomics literature with advanced analytic tools quantifies the Rate of Rise for a next generation of technologies to better define, quantify, and visualize the multiple dimensions of the proteome that will transform our ability to measure and understand proteins in the coming decade.
Research, development, testing, and operator training of large agricultural harvesting equipment has become increasingly expensive and complex. Operational simulators can offset these costs, but it is critical to evaluate the fidelity of the simulator experience to ensure that it will lead to operator behaviors that match those in the real world. This research describes a validation process for new visual cues within simulators and focuses on the presentation and fidelity of cues in a combine harvest simulator. The fully functional immersive combine simulator in this work provides a mixed-reality interface that combines a physically accurate operator interface with displays of virtual reality (VR) combine header and grain. Grain combine operators participated to assess how well they perceived the VR cues as representing actual field and crop activity. Results showed that operators successfully identified 85% of the visual cues in the combine simulator and recognized when these cues indicated machine settings adjustment needs but that operators' ability to choose the correct action to take correlated with their knowledge of the combine. Results also showed wide variation in the number of the grain combine's head reel adjustments made by operators, likely reflecting strong individual preferences or habits around reel adjustment. Operators' number of adjustment interactions with the reel was also significantly correlated with the number of correct actions chosen, suggesting that careful attention to the reel is related to good farming performance. This research demonstrates the importance of taking operator knowledge and preferences into account when designing new agricultural products and offers a systematic method of validating cues within an agricultural simulator.
Large social events can influence traffic conditions and possibly lead to jams and incidents. This study leverages crowdsourced data to analytically evaluate the relationship between social events and traffic incidents in the city of Chicago. In particular, we collected data on social events from scraping online webpages, as well as traffic data from a twitter account that posted irregular traffic incidents based on a crowdsourced navigation application (Waze). Using these two sources the relationship between social events and the occurrence of traffic incidents was investigated. The total number social events and their categories for each region and its neighboring regions were used to build models that predicted the chance of a traffic incident occurrence. Based on the analysis, we demonstrated the variables that indicated significant influence on the chance of a traffic incident occurrence in the same day, such as the number of festivals and fairs, total number of events in the (neighboring) region. We have also developed and tested several models to predict the traffic incidents. The results indicated that using solely online listed social events may not be sufficient for traffic prediction. Although the accuracies are not considerable for an independent model, it clearly indicates that the additional information provided by social events will be a valuable addition to the existing traffic prediction models.
Gaining insight into the unique characteristics of participants during user research is a valuable tool for both recruitment and understanding differences within the target population. This work describes an agricultural harvest knowledge survey that was created for user research studies that observed experienced combine operators driving a combine simulator in virtual crop fields. Two variations of the survey were designed, utilized, and evaluated in two separate studies. Both studies found a difference between low and high knowledge operators' performance on the knowledge survey in addition to performance differences. Based on the success of this survey as a population segmentation tool, the authors recommend three criteria for the design of future knowledge surveys in other domains: 1) use real world scenarios, 2) ensure question are neither too difficult nor too easy, and 3) ask the minimum number of questions to identify operator knowledge successfully. Future research aims to create a tool that can discern between system experts (with deep understanding of the system) and practice experts (who primarily have the wisdom of experience).
Chase Grimm: FAQ
Q1: What is Chase Grimm's engineering philosophy?
Chase operates from the belief that good systems engineering makes any problem approachable. His pragmatic philosophy—that different tools solve different problems and 'not everything is a snowflake'—cuts through technology hype to match the right approach to each challenge. He emphasizes pattern recognition across domains and applies the same foundational systems thinking principles whether architecting data pipelines or designing quantitative models.
Q2: What kind of work does Chase Grimm do at Consilience AI?
As a Senior Engineer at Consilience AI, Chase builds specialized language models for financial and enterprise domains. His work focuses on extracting risk signals and causal relationships from unstructured text sources including regulatory filings, earnings calls, and corporate communications. He architects text analytics infrastructure for processing these documents at scale.
Q3: What is Chase Grimm's educational background?
Chase holds a B.S. in Industrial Engineering from Iowa State University (2014-2017). During his undergraduate studies, he worked as a Research Assistant conducting user studies for agricultural technology, developed Android applications for equipment automation, and built predictive models using text mining. He also holds CAP (Certified Analytics Professional) and PMP (Project Management Professional) credentials.
Q4: What research has Chase Grimm published?
Chase has published peer-reviewed research spanning multiple domains united by underlying systems challenges. His work includes analyzing proteomics literature trends using NLP (Journal of Proteome Research, 2024), validating virtual reality simulator fidelity for agricultural equipment training (Computers and Electronics in Agriculture, 2019), investigating relationships between public events and traffic incidents using crowdsourced data (IEEE SIEDS, 2017), and developing knowledge surveys to distinguish types of operator expertise (Human Factors and Ergonomics Society, 2016).
Q5: How does Chase Grimm approach applying AI to different domains?
Chase's career demonstrates that rigorous systems thinking transfers across seemingly disparate fields. Whether analyzing proteomics literature, predicting traffic incidents, or extracting financial signals, the underlying approach remains consistent: decompose the problem, identify the right tools (rather than forcing fashionable solutions), and build infrastructure that scales. His published research spans biotechnology, agriculture, transportation, and finance—fields united by the systems challenges they present rather than their surface-level subject matter.
Q6: What experience does Chase Grimm have in financial services?
Chase spent four years as a Data & Operations Research Scientist at Principal Financial Group, applying optimization and statistical methods across investment divisions to improve portfolio performance and operational efficiency. His work spanned equities, fixed income, and asset management. This foundation, combined with his current work at Consilience AI building NLP systems for financial documents, positions him at the intersection of AI and quantitative finance.
A systems engineer with an unconventional trajectory across multiple disciplines, Chase Grimm works across the AI ecosystem—from building core text analytics infrastructure at an early-stage startup to implementing AI solutions for digital agency clients and developing algorithmic trading strategies. Their career exemplifies the power of applying rigorous systems thinking across diverse domains, leveraging the same foundational principles whether architecting data pipelines or designing quantitative models.
In a field where the half-life of knowledge is measured in months rather than years, embrace perpetual education as both a necessity and an opportunity.
Chase Grimm is a systems engineer with an unconventional trajectory spanning AI infrastructure, digital agency solutions, and algorithmic trading. Holding a B.S. in Industrial Engineering from Iowa State University, they've built a career on the belief that good systems engineering makes any problem approachable. Their published research spans traffic prediction, agricultural simulation, and proteomics—fields united by underlying systems challenges. At an early-stage AI startup, Chase architects text analytics infrastructure for processing unstructured data at scale. Their pragmatic philosophy—that different tools solve different problems and "not everything is a snowflake"—cuts through technology hype to match the right approach to each challenge. This perspective, combined with a commitment to continuous learning, positions them at the intersection of AI maturation and quantitative finance.
