Data Scientist

STG Logistics - Tropicana/Naked Juice Div.; Idaho; Remote; USD 100,000 - 125,000; Part time

Job summary

A leading logistics company seeks a Part-Time Data Scientist to join their remote Data & Analytics team. This role involves analyzing data, building predictive models, and collaborating with various teams to enhance operational efficiency. Ideal candidates will have a strong analytical background and experience in data science, with a focus on logistics.

Benefits

Competitive hourly compensation

Opportunity to contribute to high-impact projects

Collaborative team culture

Qualifications

• 2+ years of experience in a data science or analytics role.
• Proficient in Python or R, SQL, and data visualization tools.
• Strong problem-solving skills and excellent communication.

Responsibilities

• Analyze large datasets to extract meaningful insights.
• Build and validate predictive models to optimize logistics operations.
• Collaborate with teams to identify opportunities for automation.

Skills

Data Analysis

Predictive Modeling

Machine Learning

Problem Solving

Communication

Education

Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Mathematics

Tools

Python

R

SQL

Tableau

Power BI

Job description

Job Title: Part-Time Data Scientist (Remote)

Company: STG Logistics

Location: Remote (U.S.-based preferred)

Job Type: Part-Time (15–25 hours/week)

Industry: Logistics & Supply Chain

About STG Logistics:

STG Logistics is a leading provider of logistics and transportation services, specializing in port-to-door solutions across North America. With a strong network and advanced technology, we deliver customized, scalable services that help our clients streamline operations and reduce costs. Our commitment to innovation and data-driven decision-making sets us apart in the logistics industry.

Position Overview:

STG Logistics is seeking a highly analytical and motivated Part-Time Data Scientist to support our Data & Analytics team. In this remote role, you will work with cross-functional teams to develop predictive models, uncover insights, and drive operational efficiency using advanced analytics. This is a great opportunity for someone looking to apply their skills in a dynamic industry while enjoying the flexibility of part-time, remote work.

Key Responsibilities:

1. Analyze large datasets to extract meaningful insights and support strategic decision-making.
2. Build and validate predictive models and machine learning algorithms to optimize logistics operations (e.g., route efficiency, demand forecasting, capacity planning).
3. Collaborate with IT, operations, and business teams to identify opportunities for automation and process improvement.
4. Visualize data and present findings in a clear, actionable format to non-technical stakeholders.
5. Ensure data quality and integrity in all analytical outputs.
6. Stay up to date on industry trends and emerging technologies in data science and logistics.

Required Qualifications:

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Mathematics, or a related field.
• 2+ years of experience in a data science, analytics, or similar quantitative role.
• Proficient in Python or R, SQL, and data visualization tools (e.g., Tableau, Power BI).
• Experience with machine learning libraries (e.g., scikit-learn, TensorFlow, XGBoost).
• Strong problem-solving skills and a passion for finding data-driven solutions.
• Excellent communication skills and the ability to translate complex findings into actionable business insights.
• Must be self-directed and able to work independently in a remote environment.

Preferred Qualifications:

• Previous experience in logistics, supply chain, or transportation analytics.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.
• Understanding of optimization techniques and real-time data processing.

What We Offer:

• Competitive hourly compensation.
• Opportunity to contribute to high-impact projects with real business value.
• A collaborative, forward-thinking team culture.
• Exposure to a growing, technology-driven logistics leader.