Position: Data Scientist-Modeler
Location: Parsippany, NJ
Duration: Long Term Contract
As part of a team, develop advanced analytical models to solve business problems leveraging the latest technologies in statistical, unsupervised, supervised, and Artificial Intelligence (AI) models to achieve organizational goals, increase data-driven insights, functional excellence, and digital expertise.
All in Google Cloud Environment using Projects and leveraging Google Design Studio to create User Interfaces (UIs)
All models were written in Python
#1 Op Model. Connects to Google Trends for Google Search and Youtube API using a corpus of search terms for raw results, ARIMA predicted results, trends and a Granger Causality (we have algorithm) to predict future search terms)
#2 We have an operational basic Macro Economic Model that connects to 700+ FRED APIs to conduct a series of analysis:
-ARIMA by respective API
-Correlation by various series
-CCF and Lag by various series as a trend incator
#3 We have an operational model that uses batch analysis from an internal construction journal to conduct semi-active learning (supervised & unsupervised) to predict construction job value and value of numerous jobs by organization. The UI also filters data for insights for salesfolks.
#4 A testing model that uses Google AutoML to classify images
60% Data Modeling
Transform business problem into data science case to assess the scope of model’s value, speed, and cost in order to articulate model selection criteria.
Build Data Science models using cloud-computing platforms such as AWS, Azure, and Google.
Build Data Science models using any relevant Data Science capability.
Be prepared to automate analytical workflows.
Manage and shape modeling design, development, and delivery process.
20% Data Collection
Collect, profile, collate, and map appropriate data for usage in new or existing solutions as well as for ongoing data analysis activities.
Develop, maintain, and review data processes and architecture for both on-premise and cloud-based data systems.
Develop and perform standard queries to ensure data quality, identify data inconsistencies, missing data and resolve as needed.
Perform data extraction, storage, manipulation, processing, and analysis.
Conduct programmatic web scraping to acquire external publically available information.
20% Model Deployment and Evaluation
Assemble relevant User Interfaces (UIs) or Dashboards.
Assemble, deliver, and effectively communicate actionable insights for decision- makers.
LEVEL BASED COMPETENCIES
Teamwork: Collaborates with other members of formal and informal groups in the pursuit of a common mission, vision, values and mutual goals.
Change Advocate: Identifies and acts upon opportunities for continuous improvement.
Communicates for Results: Expresses technical and business concepts, ideas, feelings, opinions, and conclusions orally and in writing.
Conceptual Thinking: Applies appropriate concepts and theories in the development of principles, practices, techniques, tools and solutions.
Information Seeking: Gathers and analyzes information or data on current and future trends of best practice.
Innovation: Employs sound judgment in determining how innovations will be deployed to produce return on investment.
Problem Solving: Anticipates, identifies and defines problems; seeks root causes.
Advanced - Data intuition.
Intermediate - General experience across relevant business operations to enhance understanding would be desirable.
Entry - Exposure to roofing or manufacturing industry would be helpful
Undergraduate Education or Graduate of a Data Science Academy in a related technical Data Science discipline.