Data Scientist Job Opening
The Data Scientist is part of a collaborative team of software developers, data scientists, agronomists, and remote-sensing personnel. Functioning in “hands-on” fashion, the role contributes to building customer-facing applications and our core, proprietary platform, to ultimately create, improve, and scale agricultural models and optimization. Duties performed reflect this “hands-on” quality and functionality. They include: create, gather and process quantitative and qualitative data; perform object-oriented and functional programming using Scala; engage in rapid prototyping; perform testing and process “types” (programming language specifications to define real-world entities, their features and characteristics); engage in clustering and classification; perform multivariate regression, prediction, and forecasting; engage in dimensionality reduction and feature engineering; contribute generally to the development and engineering of production-level, parallel- and distributed-software systems; and develop an automated pipeline to interpret data science experiments. This is a full-time, permanent, immediately available job opportunity. Position is not a supervisory capacity. The Data Scientist with and at CIBO Technologies, Inc. (“CIBO” or “CIBO Technologies”), he/she does not have subordinate, direct reports. Position, as stated, consistent with operating in a start-up environment, is a “hands-on”, individual contributor again. The Data Scientist, at CIBO Technologies, just does not manage other personnel.
- Master’s Degree or foreign equivalent in computational or data science, machine learning, or related field. Also required are two years of experience working as a Data Scientist, and specifically involving two years devoted to all the following areas:
- (a) enterprise software product development (via object-oriented and functional programming using Scala), in addition to platform development specifically for machine learning applications, as well as technical architecture experience;
- (b) developing and analyzing quantitative and qualitative data, and extracting insight from large-scale, structured and unstructured data sets;
- (c) creating data ontologies and data modeling artifacts;
- (d) applying scientific methods to complex, multivariate, unstructured problems, this to produce rigorously defendable conclusions in written and graphic form;
- (e) collecting, curating, structuring, and retrieving large data sets in relational databases; and
- (f) using foundational data structures and performing programming.
Alternatively, candidates may qualify with a Doctor of Philosophy (“Ph.D.”) or foreign equivalent in computational or data science, machine learning, or related field. This would be without needing to have any post-doctoral experience at all, alongside the Ph.D. or foreign equivalent in a specified field or area. The Ph.D. or foreign equivalent, note, to be clear, would alone suffice to qualify one, provided the degree is in computational or data science, machine learning, or related field.
How to Apply
Applicants should send resumes directly to the employer at its Cambridge, MA headquarters. Specifically, can send to Human Resources (“HR”) at CIBO Technologies in Cambridge, MA, directed and addressed as follows: CIBO Technologies, Inc., HR, Attn: SB Job, P. O. Box 425910, Cambridge, MA 02142
St. Louis, MO area; CIBO has office at 20 South Sarah Street, St. Louis, MO 63108. Must consequently live in the City of St. Louis, MO or within a reasonable, daily commuting distance thereof. This is because telecommuting full-time (exclusively and indefinitely working remotely from home) historically is not permitted. While short-term accommodations have been made here (due to the pandemic), long-term in a “big picture” or permanent sense going forward, the company plans to eventually in time go back to in-house, office-based work and functionality as being the predominate, day-to-day norm. Travel, moreover, is not required.
About CIBO Technologies
We seek to catalyze a true sustainable revolution in agriculture. In order to create real change, we must account for the scientific and technical complexity of that challenge. We must also account for the complexity of a system that includes stakeholders across the private, public, and non-governmental sectors, groups that have historically struggled to find win-win solutions. We believe that there are solutions to sustainable production that leave the planet and the people better off. We are passionate about building the technology to make this a reality.