Strong Analytics is a leading provider of custom machine learning software and solutions.
Our team of machine learning scientists and engineers brings a wealth of cross-industry experience building and deploying machine learning solutions to organizations' most challenging problems.
We are building the future.
We’re dedicated to our clients. Your needs and problems always come first, and only with a deep understanding of your business can we solve those problems.
Data science is a complex and dynamic industry that’s frequently misunderstood. That’s why our credibility is a cornerstone of our identity.
Every step of our process is defined by rigor. We validate results before sharing them, and we’re upfront with clients when we come across challenges in their data.
Collaboration flows between our clients, the Strong team, and the data science community as a whole. Internally, we share what we know freely, and we’re never afraid to ask each other for help.
We care about scalability and efficiency as much as our clients do. From our project management tools to our development process to our documentation, effectiveness is at the core of what we do.
We enjoy heated video game tournaments, communicating with far too many GIFs, and getting a bit goofy.
Brock is an expert in machine learning, data engineering, and data science strategy. At Strong, he leads projects that interweave these capabilities to address challenges in technology, retail, education, healthcare, and other industries. Brock holds a Ph.D. in Cognitive Science from Northwestern University, in which he studied universal learning mechanisms underlying social cognition and language acquisition.
Jacob is an expert in computer vision, natural language processing, reinforcement learning, and leveraging artificial intelligence to automate processes in complex dynamic systems. He has led the design, development, and implementation of state-of-the art machine learning solutions for Fortune 500 companies across industries. Jacob holds a Ph.D. in Neuroscience from Northwestern University, where he developed novel deep learning based tools for decoding speech from neural signals.
Jacob is an expert in applying statistical approaches to understanding customer lifecycles; for example, predicting when and how churn happens and how to prevent it, or forecasting lifetime value and how to increase it. Jacob is also an expert in probabilistic modeling and leveraging big data to model and forecast risk. Jacob did his doctoral studies in Psychology, an M.S. in Statistics, and holds a certificate in management from the Kellogg School of Management at Northwestern University.
Juan specializes in researching and developing machine learning models, leveraging deep learning and natural language processing. Prior to joining Strong, Juan worked on text classification models for e-commerce transactions, generative models for synthetic time series generation, and 3D virtual environments for enhancing situational awareness in people with visual impairments. Juan attended Florida State University, where he received his M.S. in Computational Science and B.S. in Mathematics.
Nathan is an expert in predictive modeling, deep learning, and machine learning infrastructure. Before joining Strong, he led teams building a machine learning training and deployment platform, and predictions driving betting lines for one of the world’s largest sportsbooks. Nathan holds an M.S. in Physics & Astronomy from UNC - Chapel Hill, where he was a collaborator on a global network of ground-based optical telescopes and web interface, processing millions of astronomical images to thousands of users worldwide.
Joe brings a deep expertise in building and deploying data science and analytics projects in both the Financial and Advertising industries to his role as a data scientist at Strong Analytics. He has extensive experience in both traditional and deep learning based computer vision, and has built systems to extract text from advertisements in complex dynamic environments. Joe holds a Bachelor’s degree in Mathematics, Statistics, and Economics from the University of Chicago.
Francisco leverages his expertise in high-performance computing, computer vision, and deep learning as a data scientist as Strong. Prior to joining Strong, he developed vehicle collision claims automation tools in the insurance industry using both image and telematics data. He has also built deep learning based systems to learn and infer the low-dimensional feature dynamics of complex fluid systems. Francisco holds a Master’s degree in Aerospace Engineering from the University of Illinois at Urbana-Champaign and worked as a Computational Physics Fellow at Los Alamos National Laboratory.
Kuang specializes in deep learning, computer vision, and classical statistical inference. Prior to joining Strong, Kuang worked in the insurance space, where he optimized and enhanced a computer vision model for automobile total-loss determination. In addition, Kuang brings in unique expertise in improving the interpretability for deep learning models. Kuang holds a M.S. degree in Physics from the University of Chicago, where he was a Nambu Fellow and specialized in large-scale astronomical surveys.
Cody is an expert in working with large, heterogeneous sets of data, with experience in ingesting, transforming, and analyzing data from myriad disparate sources. Prior to joining Strong, Cody holds an M.S. and Ph.D. in Physics & Astronomy from Northwestern University, where he worked with space-based telescopes such as the Hubble Space Telescope, Planck, and Gaia, to perform broad, multi-wavelength studies of regions of interstellar space that could potentially host new star formation.
John has a broad background in statistics and machine learning, specializing in time series analysis and Bayesian statistics. Prior to joining Strong, John worked in the advertising industry where he led custom solutions for optimizing advertising campaigns. He holds a Ph.D. in Astrophysical Sciences from Princeton, where he developed a novel time-series algorithm and worked on analyzing and classifying variable stars using data from the Hungarian Automated Telescope.
Anthony is an expert in computer vision and time series analysis with a strong record of bringing AI models from concept to production. Prior to joining Strong, he has led projects ranging from real-time surface to air missile detection, optical tracking of NBA and UEFA athletes, prediction of factory machinery failure, and automated fashion-savvy outfit generation. Anthony holds a B.S. in Electrical Engineering from Marquette, where he focused on digital circuit design and antenna theory.
Cory is an expert in Bayesian modeling techniques with a specialty in handling missing and partially observed data. He has experience with a broad array of machine-learning techniques as well as traditional statistical methods. Prior to joining Strong, he was a post-doctoral researcher in cognitive science in Paris and Vancouver, focusing on how humans use their visual systems to make rapid inferences about quantity and on how to make inferences from sparse, but complex datasets that come from infants and young children. He holds an M.A. and Ph.D. in Brain and Cognitive Sciences from the University of Rochester. Cory is also a pianist and music teacher, earning degrees in performance and music theory from the Eastman School of Music.
Olesya leverages her rich multidisciplinary background and experience in R&D, new product prototyping, statistics, process automation, natural language processing and machine learning to deeply understand a new problem and build an effective solution. Prior to joining Strong, she led research and development efforts for several startups on conversational AI, material property prediction engines, and a variety of custom automated analytics and data collection tools. Olesya received a Ph.D. in Electrical Engineering from the University of California San Diego, and focused on Optics and Photonics during her graduate studies.
Michael is an expert in statistical modeling and has applied his expertise in many fields such as the social, environmental, health, and business sciences. He employs advanced statistical methods, Bayesian analysis, machine learning techniques, and visualization to help clients achieve their goals. He holds a Ph.D. in Psychology from the University of North Texas, and prior to joining the Strong team, provided expertise to the academic communities at the Universities of Michigan and Notre Dame, resulting in dozens of high quality published research products.
Cooper brings expertise in computer vision, deep learning, and large-scale data analytics. Prior to joining Strong, Cooper worked as a researcher at the University of Chicago focusing on generative neural networks and topological characterizations of urban infrastructure. He holds an M.A. in Computational Social Science, also from the University of Chicago, where he researched the use of convolutional neural networks to identify informal neighborhoods from high-resolution satellite imagery.
Noah is an expert in statistical causal inference, predictive modeling, and applied optimization techniques. Before joining Strong Analytics, he worked in the pharmaceutical and insurance industries. Noah attended Purdue University and the University of Illinois, where he received his M.S. in Statistics.
Join our growing team!
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Flexible, remote work
Work from home with 4-weeks PTO and a flexible, low-meeting work schedule.
Collaborative and growth-focused
Learn from highly-skilled colleagues through collaborative work, weekly "lab meetings", and internal knowledge shares and tools.