Devox Software
Опис
Project
Global freight management solutions and services, specializing in Freight Audit
& Payment, Order Management, Supplier Management, Visibility, TMS and Freight
Spend Analytics.
Overview
We are looking for a Data Scientist with strong background in statistics and
probability theory to help us build intelligent analytical solutions. The
current focus is on outlier detection in freight management data, with further
development toward anomaly detection and forecasting models for logistics and
freight spend. The role requires both deep analytical thinking and practical
hands-on work with data, from SQL extraction to model deployment.
Key Responsibilities
* Apply statistical methods and machine learning techniques for outlier and
anomaly detection.
* Design and develop forecasting models to predict freight costs, shipment
volumes, and logistics trends.
* Extract, preprocess, and transform large datasets directly from
SQL databases.
* Categorize exceptions into business-defined groups (e.g., High Value
Exceptions, Accessorial Charge Exceptions, Unexpected Origin/Destination).
* Collaborate with business analysts to align analytical approaches with domain
requirements.
* Use dashboards (e.g., nSight) for validation, visualization, and reporting
of results.
* Ensure models are interpretable, scalable, and deliver actionable insights.
Requirements
* Strong foundation in statistics and probability theory.
* Proficiency in Python with libraries such as pandas, numpy, matplotlib,
scikit-learn.
* Proven experience with outlier/anomaly detection techniques.
* Hands-on experience in forecasting models (time-series, regression,
or advanced ML methods).
* Strong SQL skills for working with large datasets.
* Ability to communicate findings effectively to both technical and
non-technical stakeholders.
Nice to Have
* Experience with ML frameworks (TensorFlow, PyTorch).
* Familiarity with MLOps practices and model deployment.
* Exposure to logistics, supply chain, or financial data.
* Knowledge of cloud platforms (AWS, GCP, Azure).
Global freight management solutions and services, specializing in Freight Audit
& Payment, Order Management, Supplier Management, Visibility, TMS and Freight
Spend Analytics.
Overview
We are looking for a Data Scientist with strong background in statistics and
probability theory to help us build intelligent analytical solutions. The
current focus is on outlier detection in freight management data, with further
development toward anomaly detection and forecasting models for logistics and
freight spend. The role requires both deep analytical thinking and practical
hands-on work with data, from SQL extraction to model deployment.
Key Responsibilities
* Apply statistical methods and machine learning techniques for outlier and
anomaly detection.
* Design and develop forecasting models to predict freight costs, shipment
volumes, and logistics trends.
* Extract, preprocess, and transform large datasets directly from
SQL databases.
* Categorize exceptions into business-defined groups (e.g., High Value
Exceptions, Accessorial Charge Exceptions, Unexpected Origin/Destination).
* Collaborate with business analysts to align analytical approaches with domain
requirements.
* Use dashboards (e.g., nSight) for validation, visualization, and reporting
of results.
* Ensure models are interpretable, scalable, and deliver actionable insights.
Requirements
* Strong foundation in statistics and probability theory.
* Proficiency in Python with libraries such as pandas, numpy, matplotlib,
scikit-learn.
* Proven experience with outlier/anomaly detection techniques.
* Hands-on experience in forecasting models (time-series, regression,
or advanced ML methods).
* Strong SQL skills for working with large datasets.
* Ability to communicate findings effectively to both technical and
non-technical stakeholders.
Nice to Have
* Experience with ML frameworks (TensorFlow, PyTorch).
* Familiarity with MLOps practices and model deployment.
* Exposure to logistics, supply chain, or financial data.
* Knowledge of cloud platforms (AWS, GCP, Azure).