Vacancy Machine Learning Engineer

We are looking for a highly motivated and skilled Machine Learning Engineer to lead a Proof of Concept pilot project that aims to validate the potential of using ML-based methods to predict parcel delays during transit. This project aligns with our commitment to improving digital customer service and leveraging data-driven solutions to enhance our unique selling points.

Key Objectives:

• Establish a Machine Learning model that calculates the probability of parcel delays;
• Determine the business value of the calculated probability;
• Identify the most suitable Machine Learning model considering data limitations and time constraints.

Scope:

• Utilize raw data events related to parcel journeys from various integrators;
• Enrich the dataset with additional variables such as actual departure, delivery and transit time, parcel delay status;
• Incorporate secondary data, including service level, destination, customs value, and dimensions;
• Work with a dataset containing at least one month's worth of data (80,000-100,000 parcels).

Deliverables:

• Develop a real-time Machine Learning model for estimating parcel delay probability;
• Continuously update the probability from shipment to delivery, triggered by new events and at set intervals;
• Present results in a confusion matrix to evaluate model accuracy comprehensively;
• Provide a final presentation of the ML model, its accuracy, and critical findings;
• Offer recommendations for further model refinement;
• Facilitate model handover and documentation.

Process Model:
The Machine Learning Engineer will follow a structured process involving:

• Exploratory Data Analysis: Gain a high-level understanding of the dataset through visualizations and domain expert feedback;
• Selection of ML model: Choose the appropriate ML model;
• Data Transformation: Prepare the data for ML model training;
• ML Model Building: Train the ML model on the dataset;
• ML Model Evaluation: Assess model accuracy using test data;

Qualifications:

• Bachelor's degree or higher in Computer Science, Machine Learning, or related field;
• Proven experience in Machine Learning model development and deployment (2-3 years);
• Proficiency in programming languages such as Python or R;
• Strong analytical and problem-solving skills;
• Excellent communication and presentation abilities;
• Experience with data analysis tools and libraries (e.g., Pandas, NumPy, Scikit-Learn);
• Familiarity with data visualization tools (e.g., Matplotlib, Seaborn);
• Knowledge of cloud computing platforms (e.g., AWS, Azure) is a plus;
• Spoken level of English (upper-intermediate).