Our client is a startup that is working to revolutionize the field of business optimization. To help, they’ve asked us to find them the proverbial unicorn - someone who is super-strong in both operations research and in data science. The ideal candidate will be a strategic leader who will work closely with both internal and client teams to analyze their complex business problems and apply Data Science and Operations Research methods to develop the innovative optimization models to solve them.
Is this you? If so, please submit your resume and fill out our questionnaire ASAP!
THE WORK: The work includes:
- Complex data analysis and the development of new optimization algorithms for problem solving
- Collaborating with clients and business partners to help them develop customized solutions
The chosen consultant will be working with a highly skilled team to use data science techniques to help drive the next generation of optimization technologies.
LOCATION: Our client is located in St. Louis, Missouri and while they’d prefer the consultant to work there, they are open to remote assignments for uniquely skilled individuals.
MODE: This role will require approximately 20 hours per week for 2 - 3 months. Followup assignments are possible.
REQUIRED: Applicants must have a Masters or PhD in a quantitative field (Ops Research, Statistics, Applied Math, etc.) and be very strong in the following skills:
- Operations research
- Statistical analysis, machine learning, and optimization modeling
- Python and / or C++
DESIRED: In addition, it would be great if you had:
- Building and developing software for cloud deployment
- Big Data platforms/tools such as AWS, Hadoop, Spark
- Data visualization
COMPENSATION: The hourly rate is negotiable and the chosen candidate will be able to separately charge travel expenses should those be required.
INTERESTED? If you're interested and have the skills, we'd love to hear from you. Please answer our questionnaire and submit your resume right away! Thanks!
NOTE: Dataspace performs background checks on accepted candidates prior to their employment or contract start dates.