The need for big data is a hot business topic, but the common reality is that most companies are actually drowning in data. What they seek is knowledge. Applicable. Usable. Meaningful. Knowledge.
The reason for this is clear. While it is called data science, it is also art: It demands insight, experience, creativity and vision to produce winning solutions.
Ars Quanta provides data smarts for smart businesses.
We craft custom solutions that consistently deliver measurable results. Our seasoned, multidisciplinary team delivers the big data, decision-science tools you need to power growth, improve efficiency or gain greater visibility into your business.
Solutions for your most challenging and complex problems:
Companies forecast product demand, inventory levels, consumer activity, and many other things. With the right data and domain expertise, we can fit predictive models to generate a forecast to inform business decisions.
Understanding markets and potential consumers of products is critical. Using data science and machine learning, we identify natural activity groupings and consumer types to inform customer communications and product strategies.
Measuring consumer activity and product consumption is critical to fostering growth. Affinity modeling predicts other market segments that are likely to have similar interests in a product or service.
Organizations need to model less-than-desirable outcomes in order to create risk mitigation strategies. We employ a number of modeling tools to assess risk ranging from probability of financial default to operations timelines.
An Approach Built Just for You
Ars Quanta applies advanced statistics, econometrics, AI and machine learning methodologies to internal and external data sets in order to find data-driven solutions to specific business problems. This follows a four step process:
First, we define the formal business problem to be analyzed or predicted and align project sponsors and team with the project objectives.
Second, we conduct a thorough evaluation of the available data sources - both internal and external - for applicability to the model.
Third, we apply cutting-edge techniques to optimize and evaluate different predictive models to get to the best solution.
Fourth, we employ an agile approach to projects through iterative development and A/B test-and-learn methods.
A Compelling Mission. A Passionate Team.
We actively pursue projects that leave marketplaces, communities, customers and our clients in a better state. Our mission is to enable more optimal decisions based upon data by applying advanced data science, analytics, and quantitative modeling techniques to a broader range of organizations that don’t otherwise have access to them.
Chief Data Scientist
Jay combines the technical skills of a Ph.D. Machine Learning Scientist with the seasoned maturity from 20+ years in industry. He has delivered data-driven insights and value to Fortune 500 companies and startups across a range of industries. Prior to joining Ars Quanta as Chief Data Scientist, he was the Senior Research Data Scientist at Zignal Labs, helping their enterprise customers understand and reach out to their customers by training neural networks to determine the topics and sentiment of millions of social media, news and blog posts about these enterprise companies. Prior to that he worked on Apple’s autonomous car project as a Research Scientist experimenting with a range of deep learning architectures, training the neural networks to recognize a range of objects in scenes.
His work has focused on creating machine learning systems for pattern recognition, primarily in the domains of image understanding and text classification. He has worked on projects as diverse as developing machine learning systems to detect spam for Microsoft and infer key life events like getting married from social media posts for Hearsay Systems, to detecting important classes of objects in images as Chief Scientist for iComprehend.
Founder and CEO
With two decades of experience in analytics spanning retail, CPG, advertising, marketing, and financial services, Jason has worked on all sides of enterprise analytics—managing teams in data engineering, data warehousing, product management, analytics, business management & operations, R&D, & Applied Sciences—Jason has held numerous executive roles from director to SVP including roles at companies like Amazon, Microsoft, Starbucks, Disney, and Accenture.
Jason has contributed to multiple patents and has served as an advisor to several startups. He holds a Bachelor of Science degree in Systems Engineering and International Relations from The United States Military Academy at West Point.
Principal Data Engineer￼/Architect
Michael is our Principal Engineer at ArsQuanta. Prior to joining Ars Quanta, Michael worked as Software Engineer with companies like Starbucks and Cambia health as well as multiple start-ups and smaller companies. His work in data mining, ETL, business analytics, privacy, security, BI solutions, AWS/cloud, data warehousing, database and software development make him a significant value add to our engineering efforts supporting Data Science. Michael is also a Databricks Certified Developer - Apache Spark 2.x for Python.