The Silicon Review
In the highly competitive world we live in, data can be used by businesses to determine the profitability of specific actions, products, or services, as well as the potential areas of their biggest expenses. Lowering costs is usually the key to boosting profitability because companies can cut costs and keep more of the money they make by doing so. Globally there are various companies helping businesses find actionable insights from their data, but Mosaic Data Science stands out from the rest. Mosaic Data Science designs and deploys actionable artificial intelligence and machine learning solutions for organizations that want to use their data to make more informed decisions.
The company’s team of data scientists collaborates with businesses in every industrial segment, enterprise, or startup, to build predictive & prescriptive analytics unique to the input data, desired outputs, and unique decision-making processes presented by the client environment. Mosaic Data Science also believes that data science should be available at scale to all firms, whether you are just starting to think about it or already have an established team. Customers like the company’s practical approach to themes like digital transformation, generative AI, and supply chain optimization. Mosaic Data Science boil more significant initiatives into bite-sized proofs of concept that deliver value in weeks or months, not years. Drew Clancy, VP of Marketing & Sales states that “Fortunately for Mosaic, there is no substitute to enable artificial intelligence at organizations without the trained hand of a skilled data scientist. No single piece of software can capture the sheer number of algorithmic possibilities that exist when leveraging data to make a prediction or recommendation.”
Mosaic also promotes ethical, explainable, and sustainable AI/ML solutions. MLOPs, model monitoring, and cost-effective data architectures are baked into every production-grade solution delivered.
When The Silicon Review asked about Mosaic Data Science’s experts and expertise, its CEO, Chris Brinton, stated, “We recognize the AI and ML field as a growing and highly competitive industry. To attract top talent, we offer remote employment opportunities for data scientists looking to solve challenging data problems across several different clients and problem sets. Our data scientists work on different projects, which allows them to be exposed to several use cases rather than solving the same problem over and over again.” Furthermore, he added, “When we hire new data scientists, we ensure they have a minimum of two years of professional experience leveraging open-source languages like Python, TensorFlow, R, etc. We find that focusing our hiring efforts on data scientists that have real-world experience benefits our clients as they can quickly spin up on the use case and requirements as compared to recent grad school or PHD level candidates, as the data we encounter in different client environments is hardly as straightforward as typically found in an academic problem or exercise.”
Mosaic continues to invest in emerging markets and seeks data scientists with specialized skills. A few years ago, the company made a big investment in hiring data scientists with Natural Language Processing and Computer Vision skillsets, which has led to Mosaic developing robust solutions in those areas. The company is always looking forward to what is coming next in the AI and ML world.
Data science, in its nature, is utilizing the scientific process to prove or disprove a hypothesis. There is no guarantee that based on the data you have today that you can predict any outcome, but you have methodically go through the analysis process to determine if your desired outputs can be generated. Even if a project isn’t feasible with existing data, more often than not, the modeling process can guide you to concrete next steps, whether it be collecting more data or labeling the data properly.
Chris Provan, Chief Data Scientist of Mosaic Data Science states that “Much of the predictive or prescriptive analytics problems the business world thinks of are more scientifically phrased as, based on this sample size and attributes of a population (training data), can I predict an outcome with a level of statistical confidence. All sorts of insights fall out in the algorithm training/validation process, such as these set of variables providing the most predictive power, which in itself, provides the end user power. Exploratory data analysis can often help companies tease out hidden trends, causations vs. correlations, etc.”
Upcoming services and future
Mosaic Data Science is working on an operating manual search engine solution for heavy industrial production companies. When you think about the operating manuals these organizations need to construct and deliver, well, it can be a headache to try and find the information you seek. Chris Provan added that “Mosaic has reverse-engineered search engines for a few customers that take either a typed or spoken search query, scan millions of documents, and retrieve the desired information in a matter of seconds. We think this will fundamentally change how heavy industrial companies operate, as this will become the customer experience expected, which leads to safer and less time-intensive operational environments.”
Mosaic had been leveraging large language models, transformer-based architectures, and Chat GPT3 to build custom NLP solutions for its customers long before the service was “launched” to the public. As it continues to grow, Mosaic will bring more value to its customers through more repeatable industry solution frameworks, allowing for even more efficient and effective project flows. The company has also hired a data scientist with deep knowledge of network science and graph analytics, as it thinks teasing out the relationships between networks can benefit so many projects. Mosaic continues to push the innovation needle on what is possible with IoT. The company found that some have grown tired of the promise of IoT data, but its customers remain quite satisfied with what they can do with these sensor streams.
Mike Shumpert, Managing Director of Data Science, stated that “Our marketing has definitely started to benefit our hiring practices, as we find our employment pipelines overflowing with excellent candidates, and we can’t wait to bring on more and more data science talent to deliver for our new and existing customers.”
Meet the leaders behind the success of Mosaic Data Science
CHRIS BRINTON CEO | Founder
More than two decades of R&D experience in logistics. Founded Mosaic Data Science in 2014 and Mosaic ATM in 2004. Leads numerous analytics projects focused on improving the efficiency and safety of the National Airspace system. MS Electrical Engineering, Stanford | BSE Mechanical and Aerospace Engineering, Princeton.
MIKE SHUMPERT Managing Director Data Science
Over two decades of product/service development across diverse industries. Solid record of applying data science to produce double-digit revenue growth and cost savings in both small and large companies. Formerly VP of Analytics for Software AG and VP of Global Product Management for Dun & Bradstreet. MBA, Georgetown | BS Systems Engineering (Operations Research), University of Virginia.
CHRIS PROVAN Chief Data Scientist
Over 10 years successfully leading and executing advanced analytics projects for customers in a wide range of industries. Organizational analytics strategy coach and data science training instructor. Expert at integrating advanced statistical, optimization, and computer science techniques into decision processes to drive measurable impact on business outcomes. MS Operations Research, Cornell | BS Math Vanderbilt | INFORMS Certified Analytics Professional.
DREW CLANCY VP of Marketing & Sales
Marketing professional with 10+ years of experience in helping companies grow through innovative marketing program design. Sets marketing strategy and executes that plan through a test and learn methodology. Interacts with prospects and clients. MBA Marketing Management, Business Analytics Syracuse University | BA English/Journalism, University of New Hampshire.