| Data Analyst : Richmond, VA (Hybrid) : 12 Months Contract at Richmond, Virginia, USA |
| Email: [email protected] |
| From: Jitendra Upreity, Technocraft Solutions [email protected] Reply to: [email protected] Job Title: Data Analyst (Local Only) Location: Richmond, VA (Hybrid) Duration: 12 Months Contract Note: *****local Richmond, VA candidates required due to onsite requirement *Candidate must also be able to work onsite 2-3 day/week *Parking not provided Responsibilities and Skills: Job ID: 723393 Location: Richmond VA (VDOT) Duration: 12 months Skill Required / Desired Amount of Experience Data retrieval (UI, SQL, API) Required 3 Years Data Engineering (cleaning, preparation, validation) Required 3 Years Data analytics, integration, and visualization Required 2 Years Advanced Excel skills Highly desired 3 Years Visualization platforms: Tableau, PowerBI Required 2 Years Scripting/coding for data science: Python, R Highly desired 2 Years GIS tools: ArcMap, ArcGIS Pro Highly desired 1 Years Exposure to some transportation skills & concepts Desired 2 Years Data fusion Desired 1 Years Data science (model development, applying machine learning to transportation problems) Nice to have 1 Years Map conflation Nice to have 2 Years Attention to detail Required 2 Years Strong organizational skills Required 2 Years Ability to work independently Required 2 Years Ability to present data in a format understandable to diverse audience Highly desired 1 Years Strong communication skills Desired 1 Years Perform Traffic Data Analytics tasks like data acquisition and cleaning; processing; validation; fusion & conflation; analytics, reporting & visualization; prototype pilot VDOT projects; and ad-hoc analyses. The selected Program Analyst Traffic (Part-Time) will be engaged in various aspects of Traffic Data Analytics tasks including but not limited to the following: Data acquisition, cleaning and preparation tasks require working with many transportation and non-transportation data sets e.g. speed, volume, AADT(Average Annual Daily Traffic), road geometry and characteristics, socio-demographics data, posted speed limits, weather, work zone information, crash and incident data, pedestrian and bicycle data, and others, and obtaining those through different means such as: graphical user interfaces, APIs, direct access to back-end databases either internally from VDOT or from third party vendor and archives. Data cleaning and preparation steps will be needed, especially for free-form text fields, and they may be different based on the data set and source. Data fusion tasks require merging or aligning (fusing) data sets on the spatial(location) or temporal (point in time or similar period of year and time of day)dimensions, in a redundant (same attributes) or complementary (different attributes) fashion. Data analysis and visualization tasks will require the incumbent to apply tried-and-true but also new-and-innovative methods to identify historical travel trends, conduct Before-and-After studies, perform congestion and reliability analyses for specified areas and time periods of interest, identify locations and time periods experiencing unusual traffic behavior. Analysis results will be presented in a format and at a level of detail appropriate for the audience, through interactive and static charts, graphs, and maps. GIS and map conflation will require leveraging existing map conflation tools and algorithms, but in some cases development of new algorithms and methods maybe necessary. Either way, manual checking and validation of conflation will be needed since conflation algorithms are not perfect and do not cover all real-life situations. Some research to perform the above tasks will be needed. Research will include but not be limited to literature review of transportation data analytics algorithms, investigating the feasibility and usability of incorporating new data sources into analytics algorithms to improve accuracy and relevance of results, traffic forecasting methods. Keywords: user interface active directory rlang Idaho Virginia |
| [email protected] View all |
| Fri Nov 03 01:33:00 UTC 2023 |