Develop and deploy advanced forecasting models for sales, inventory, transportation, and transit time estimation to improve accuracy and operational efficiency.
Collaborate closely with fulfillment, transportation, revenue, product, engineering, and data science teams to translate business needs into predictive solutions that drive cost savings and enhance customer experience.
Own machine learning projects end-to-end—from ideation, data exploration, and prototyping to production deployment, monitoring, and continuous improvement.
Identify opportunities for optimization by leveraging predictive analytics to reduce costs, improve inventory placement, and minimize transit times across ShipBob’s global network.
Communicate insights and recommendations effectively through reports, dashboards, and presentations to senior stakeholders (Director to C-Suite), influencing strategic choices.
Promotes best practices in modeling and experimentation, ensuring scalability, reliability, and measurable impact.
Additional duties and responsibilities as necessary.

4+ years of industry experience in data science, machine learning engineering, or technical leadership roles.
Proven ability to design, prototype, scale, and launch data science solutions that address real business needs.
Strong understanding of the machine learning lifecycle, including best practices, algorithms, and domains such as anomaly detection, NLP, computer vision, personalization, recommendation, and optimization.
Experience collaborating with cross-functional stakeholders and leveraging domain expertise to deliver impactful solutions.
Excellent programming skills in Python and familiarity with software engineering principles (testing, code reviews, deployment).
Working knowledge of data engineering fundamentals, including building data pipelines, processing, and storage.
Product-oriented mindset with the ability to apply conceptual and innovative thinking to create user-focused solutions.
Clear and effective communication skills for audiences at varying technical levels.
Graduate degree (MS/PhD) in a quantitative field (engineering, computer science, machine learning, operations research, statistics, mathematics) is a plus.
Experience with applied machine learning and cloud deployment (preferably in the Azure ecosystem) is highly desirable.

Data Scientist III, Forecasting

Key skills