Key Responsibilities
- Design, build, and maintain analytical datasets, dashboards, and KPIs within Domo to drive insights for internal teams and external clients.
- Write and optimize SQL queries in Amazon Redshift to extract, transform, and aggregate parcel-level and carrier data.
- Develop Python-based models and scripts for predictive analytics, classification, and anomaly detection (e.g., delivery time predictions, cost benchmarking).
- Integrate AI and machine learning tooling (e.g., CatBoost, scikit-learn, or large-language-model APIs) into production analytics workflows.
- Partner cross-functionally with Product, Sales, and Operations to turn data into actionable insights that inform product direction and carrier negotiations.
- Support ad-hoc data requests, automate recurring analyses, and ensure data quality across pipelines.
- Continuously evaluate model performance and retrain algorithms to maintain accuracy amid seasonal shipping fluctuations.
Required Qualifications
- Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or related field or 2–5 years of experience in an analytics or data science role.
- Strong proficiency in SQL and Python (pandas, NumPy, scikit-learn).
- Experience working with Amazon Redshift or equivalent cloud-based data warehouse.
- Proven experience building dashboards in Domo or similar BI platforms (Power BI, Tableau, Looker).
- Solid understanding of data modeling, ETL, and version control (Git).
- Strong communication skills — able to translate technical findings into clear business insights.
Preferred Qualifications
- Experience in logistics, eCommerce, or shipping analytics.
- Familiarity with predictive modeling, confidence scoring, or time-in-transit analysis.
- Exposure to LLMs and AI toolchains (OpenAI API, Hugging Face, etc.) for data automation and reporting.
- Working knowledge of Domo Bricks, Python scripting in Domo, or API integrations.
What You’ll Get
- Opportunity to work on cutting-edge analytics and AI initiatives in the shipping tech space.
- Exposure to large-scale, high-velocity datasets with direct business impact.
- Collaborative team culture focused on innovation, learning, and performance.
- Competitive salary, flexible schedule, and room for rapid growth within eHub’s data organization.
Job Types: Full-time, Permanent
Benefits:
- 401(k) matching
- Dental insurance
- Food provided
- Health insurance
- Paid holidays
- Paid time off
- Parental leave
- Professional development assistance
- Vision insurance
Work Location: In person