Job ID: VA-641310 (912390105)
Data Scientist with machine learning, python, R, deep learning, text mining, ArcGIS, PowerBI, Tableau experience
Location: 11401 E Broad St
Richmond, VA 23219 (VDOT)
Duration: 6+ months
Interview : Webcam Interview Only
Knowledge/experience in statistical modeling, data mining and ML using tools/techniques, Python, R, deep learning, text mining, graphic analysis Required 3 Years
Geospatial analysis packages: ArcGIS or similar products Highly desired 1 Years
Business intelligence packages: Microsoft PowerBI, Tableau, or similar products Highly desired 2 Years
• Must be self-driven, curious, creative and a good communicator • Demonstrate the ability to work in diverse, cross-functional teams. • Required 3 Years
local candidates strongly preferred
*interviews will be conducted via Skype or Google Hangout.
*candidates will telework now (after coming to office to pick up laptop). Once restrictions are lifted, candidates will be required come to Richmond offices once in a while for face to face team meetings.
As a member of the VDOT Data Science team, selected candidate will lead data science projects and initiatives using ML/AI methodologies, techniques and models in a cloud environment.
Virginia Department of Transportation (VDOT) is an organization with 7,800 employees and a $6 Billion annual budget is looking to fill the position of a Data Scientist consultant. The data scientist is a role in the Office of Strategic Innovations in VDOT and reports to the Data Science Program Manager. Data science team will play a pivotal role in planning, executing and delivering machine learning-based projects to improve highway safety, reduce congestion and improve business processes. The bulk of the work will be in machine learning (ML) modelling, management and problem analysis, data exploration and data correlation and clustering.
The newly hired data scientist will be a key interface between the Office of Strategic Innovations and analytics teams and leadership in the business units, and the Information Technology Division. Candidates need to be very much self-driven, curious and creative.
Acquire access to various databases, and other source systems. Help to create data pipelines for more efficient and repeatable data science projects. Apply statistical analysis and visualization techniques to various data.
Network with domain experts to better understand the business mechanics that generated the data. Apply various ML and advanced analytics techniques to perform classification or prediction tasks. Integrate domain knowledge into the ML solution. Collaborate with ML operations (MLOps), data engineers, and IT to evaluate and implement ML deployment options. Continuously monitor execution and health of production ML models. Establish best practices around ML production infrastructure