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’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.
Nina is a Senior ML Scientist at Strong Analytics. Previously, she was a Research Scientist at the University of Dayton Vision Lab where her work focused on deep learning for remote sensing and geospatial intelligence. She has over 7 years of experience in the computer vision space and obtained her Ph.D. in 2022.
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.
James is an experienced ML scientist who has worked across a range of industries over the last decade, with extended spells in the environmental, energy and defense sectors. He is passionate about applying the latest methods and best practices in ML and data engineering to solve complex business problems. James has a masters in geophysics and a bachelors in physics, both with honors.
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.
Osman has worked on a broad range of Perception and Machine Learning tasks, including sensor optimization and calibration, large scale synthetic dataset creation, and object detection and text reading, originally in the domain of autonomous vehicles. He holds a B.A. in Physics from Harvard University and a M.S. in Computer Science from Georgia Tech. Prior to his focus on autonomy, he worked in strategy consulting as well as consumer hardware, including designing a smart cane to improve senior citizen mobility and access to emergency support.
Grant (he/him) marries his passion for science and love of technology to build models to help people piece together the past, understand the present, and predict the future. Prior to joining Strong, he worked in the transportation space maintaining, enhancing, and bringing to scale nationwide models of population dynamics. Additionally he previously developed and/or utilized models to characterize new states of matter for water, study fly swarm self-organization, discover nanoscale bio-sensors for NASA, and understand how infectious diseases propagate through a population. He holds a M.Sc. in Physics from the Universität Stuttgart with a concentration in computational methods, where he also completed a Fulbright Research Fellowship (2017 cohort).
Andy is a Machine Learning Scientist at Strong Analytics. Prior to joining Strong, Andy was a technical consultant on AI/ML projects in a number of industries, including the oil & gas, transportation, insurance, and manufacturing sectors. He has expertise in applying reinforcement learning, machine learning, optimization, and other AI methods to solve data-driven problems. Andy has a Master's degree in Computing Science from the University of Alberta, with a specialization in Statistical Machine Learning. Under the supervision of Prof. Osmar Zaïane and an industrial sponsor, he developed a novel method for fault detection in mechanical equipment. Andy spends his spare time watching (and sometimes playing) hockey.
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.
Elizabeth grew up loving all subjects in school and went on to get degrees in the disparate fields of archaeology, education, and applied statistics. So, when she started doing data science, she readily came to love its interdisciplinary nature - data science can be applied to myriad data, regardless of its content area. Elizabeth has used data science techniques to solve the problems of organizations in many industries, including education, human resources, and publishing. When she isn’t studying data science, she enjoys reading novels, biographies, and science articles, as well as planning her next trip to a foreign destination, such as Mumbai, India or the Great Barrier Reef in Australia.
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.
My name is Mike and I’m joining Strong as a Machine Learning Engineer. I’m joining you from Austin TX, where I’ve lived now for about 2 years after moving from Chicago. I bring with me 10 years of experience, mainly working as a back-end developer for several companies, some large, some small. I started pushing my career towards data science work in the past few years, and so far, most of my experience has been within the domains of univariate time-series forecasting and record linkage / entity resolution. Beyond that, I’m passionate about anything involving reinforcement learning or population-based algorithms. When I’m not working, I like to play chess or some musical instrument (I mainly play guitar and started learning piano recently). I also have two cats, their names are Nugget and Noodle.
Join our growing team!
We offer premium health insurance, dental insurance, and vision insurance for you and your family.
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.