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Data Scientist, Machine Learning (Part Time) - Job Opportunity at Ever.Ag

Remote, US
Part-time
Mid-level
Posted: May 6, 2025
Remote
USD 60,000-75,000 annually (pro-rated for part-time)

Benefits

Remote work flexibility
SaaS industry experience
Professional development opportunities
Collaborative work environment
Part-time schedule flexibility (20 hours/week)

Key Responsibilities

Design and implement machine learning models for enterprise applications
Develop and maintain data pipelines and ETL processes
Collaborate with customers on implementation and support
Conduct statistical analysis for market insights
Configure and deploy ML solutions in production environments
Present findings to customer leadership teams

Requirements

Education

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

Experience

2+ years experience in building machine learning and/or forecasting models

Required Skills

Machine learning model development Data pipeline design Statistical analysis SQL Python GitHub AWS (preferred) ETL processes Customer communication Problem-solving Mathematical and statistical concepts Critical thinking
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Sauge AI Market Intelligence

Industry Trends

AgTech sector is experiencing rapid digital transformation with increased focus on ML applications Part-time data science roles are becoming more common as companies seek flexible expertise Growing demand for ML specialists who can bridge technical implementation with business value Rising importance of sustainable agriculture technology solutions

Role Significance

Likely part of a small to medium-sized data science team given company profile and role responsibilities
Mid-level individual contributor with significant customer interaction and technical ownership

Key Projects

Agricultural forecasting models Supply chain optimization algorithms Customer-specific ML implementation projects Data pipeline development for agricultural analytics

Success Factors

Strong balance of technical ML expertise and customer communication skills Ability to translate complex technical concepts for business stakeholders Experience with agricultural or supply chain data preferred Comfortable working independently in a remote environment

Market Demand

High demand with growing emphasis on specialized AgTech experience and customer-facing capabilities

Important Skills

Critical Skills

Machine learning implementation experience Python and SQL proficiency Statistical analysis capabilities Communication skills for customer interaction

Beneficial Skills

Agricultural domain knowledge AWS cloud platform experience Project management skills Business acumen

Unique Aspects

Direct customer interaction unusual for ML role indicating strong business focus
Specialized domain knowledge opportunity in agriculture technology
Flexible part-time arrangement shows modern work approach
Combination of technical depth and business impact

Career Growth

2-3 years in role before advancement, depending on impact and business growth

Potential Next Roles

Senior Data Scientist ML Team Lead Technical Product Manager AgTech Solutions Architect

Company Overview

Ever.Ag

Ever.Ag is a specialized AgTech solutions provider focusing on agriculture, food, and beverage supply chains

Established player in the agricultural technology sector with comprehensive solution portfolio
US-based with likely national coverage in agricultural regions
Technology-driven environment with emphasis on innovation and customer success
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Data Sources & Analysis Information

Job Listings Data

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Sauge AI Market Intelligence

Our advanced AI system analyzes each job listing to provide valuable insights including:

  • Industry trends and market dynamics
  • Salary estimates and market demand analysis
  • Role significance and career growth potential
  • Critical success factors and key skills
  • Unique aspects of each position

This integration of reliable job data with AI-powered analysis helps provide you with comprehensive insights for making informed career decisions.