Making lives better through knowledge is my mission. I specialize in analytics, statistics, and data science because it gives empirical evidence as to what solutions can help, and what does not. These "solutions" may be automation applications, predictive models, or research into new ways to solve old problems. However, if people cannot understand a solution then they probably won't use it. Alternatively, if they just blindly use a solution, then they are making their jobs replaceable.
Therefore, I consider equally important both the acquisition of knowledge and the communication of said knowledge.
Additionally, I cannot solve most problems by myself, despite my inclination to try sometimes. Begrudgingly, leadership and management is the third requirement if I'm going to have any chance of helping the world with knowledge - creating a trifecta of aspirational skills in Leadership, Communication, and of course, the Technical Ability to actually do all the stuff I'm talking about and attempting to achieve.
Some would call this "Strategic Data Science" or "Innovation Management" or some other such buzzword. I don't care what it's called, what I care about is learning, doing, and teaching - and while I may not always be successful in my attempts, here's a few examples of what I have been able to do so far.
- Provided data science strategy and established data science competencies in Health Insurance, Oil and Gas, Manufacturing, Telecom, Marketing, and Professional Service organizations.
- Personally designed and implemented Analytic solution delivery framework used in over one hundred successful data science Use Cases, POCs, POVs, and production solutions. Founded data science team and grew to multi-millions in billings in under three years.
- Designed innovation knowledge development plans leading to ALS distributed recommendation engine and Neural Network classification models in 2015; Bayesian Multi-Level Non-Linear Mixed-Effects Forecast, Classification, and Propensity Models in 2016; as well as Hypergraph multi-channel Bayesian attribution and natural language understanding data models for AI bots.
- Strategic lead in developing new competencies for managerial accounting and longstanding member of leadership academy and technology practices committee at Institute of Management Accountants.
- Presented and sold analytic solutions to clients and potential clients at multiple Fortune 500 companies.
- Multiple magazine and online articles on Data Science.
- Active member of Toastmasters.
- Designed communication framework for facilitating workplace innovation.
- Instituted professional development programs for multiple businesses.
- 2013 Recipient of IMA's Young Professional of the Year.
- Presented leadership and technology at webinars and conferences to tens of thousands of attendees.
- Full-Stack data scientist with solutions architected and deployed in Azure, AWS, MAPR, HortownWorks, and traditional single server solutions.
Models Development using R, Python, scala libraries such as PySpark, SparkR, MlLib, tensorflow, vectorflow, caret, STAN, Photon ML, and Prophet include:
- ALS and Shallow NN recommendation engines
- GA, polynomial, and Linear Multi-Level Mixed-Effects models for price, demand, marketing, and production forecasting
- Convolutional and Recurrent Neural Networks for pattern recognition and classification
- Multi-Channel Bayesian Attribution Models
- Latent Dirichlet Allocation NLP and SEO models
- Non-linear decline curve analysis
- Graph and Hypergraph data models in Neo4j, GraphX, and GraphFrames
- Administrator and SME for several data science and analytic platforms: Tableau, TIBCO Spotfire, Google Tag Manager, Google Analytics, WebTrends.
My Elevator Pitch: On a mission to make the world a better place with Data Science, Daniel applies technology and mathematics to make business and individuals faster and smarter. Daniel has led and managed solutions for diverse client sectors such as manufacturing, telecom, advertising, military, insurance, and oil & gas. These solutions include everything from Artificial Intelligence self-learning natural language processing engines to streaming cloud analytics. Although the analytic solutions are often mathematically complex, Daniel’s experience leading organizations into new levels of analytics maturity ensure solutions developed by him and his teams are relevant, valuable, and simple to understand.