Data Scientist, Machine Learning

Internal Job Title: Data Scientist I or II 

Reports To: Senior Data Scientist 

FLSA Status: Hourly, Non-Exempt, Part Time 

Location: Remote 

External Job Title: Data Scientist, Machine Learning | Part Time 

Position Summary 

The Data Scientist ML, part time, role will work on training ML models using data pipelines, Extraction Transformation Loading (ETL) processes, and data integration. This role will also deliver excellent customer experience by working directly with clients and customers.  The role would need a commitment to 20 hours a week. 

Key Responsibilities 

• Projects involving machine learning (ML) models in applications areas, estimating the performance gain and building a case for further development.  

• Build machine learning components for the Ever.Ag Data Science application platform.  

• Help configure and deploy ML models in customer implementations. 

• Help support pre-existing customer implementations. 

• Utilize statistical analysis to identify trends in customer and market data. 

• Collaborate on project data requirements, gather and validate information, and apply judgment and statistical tests to devise problem-solving actions. 

• Perform tests to ensure the accuracy of the models. 

• Work with existing tools and modeling techniques to solve the problem at hand. Work with a team of services professionals to deliver high-value products in a Software as a Service (SaaS) environment. 

• Report to the customer leadership teams on solutions and assist with decision making. 

• Potential to drive to and from customer or job sites, while safely operating a company-provided or personal vehicle and adhering to all traffic laws and safety regulations, including no handheld phone use while driving.

• Other duties as assigned. 

Qualifications 

• Bachelor's or higher degree, preferably master’s or Ph.D., in a relevant field (e.g., Data Science, Computer Science, Engineering, Statistics). PhD candidates welcome. 

• Typically, 2+ years’ experience in building machine learning and/or forecasting models for academic/enterprise applications. 

• Experience implementing a breadth of different modelling approaches/ techniques in machine learning. 

• Experience manipulating and preparing large data sets to support advanced analytics. 

• Demonstrated ability to iteratively conceptualize, design and build data pipelines. 

• Hands on experience with common analysis and version control tools (SQL, Python, GitHub). 

• Demonstrable familiarity with code and programming concepts. 

• Collaborative, open, and respectful working style.

• Familiarity with cloud-based technologies such as AWS is a plus.  

• Must be able to operate a motor vehicle safely and legally while complying with all applicable traffic laws and company policies, including the use of hands-free communication devices.
• Must maintain a valid driver's license and an acceptable driving record.

Competencies for Success 

• Excellent written and verbal communication: Presents oneself clearly and articulately when speaking, assuring that others fully comprehend the intended message; Uses appropriate grammar tailored to the audience    

• Analytical and Critical Thinking: Review and manage data with strong attention to detail; combine facts with likely possibilities; articulate and resolve complex problems    

• Quality Focused: A recognition of the value of doing things the right way; having a high sense of integrity and thoughtfulness in your actions    

• Math Ability: Ability to calculate figures and amounts. Ability to work with mathematical concepts such as probability and statistical inference.     

• Reasoning Ability: Ability to think critically and solve problems with a variety of variables in situations where, at times, only limited standardization exists. The ability to define problems, collect data, establish facts, and draw conclusions. Ability to interpret a variety of technical instructions furnished in written, oral, diagram, or schedule form.      

• Action Oriented: A bias for action, when you see a problem, you solve it using your technical savvy and internal resources