POSITION OVERVIEW
As a data scientist, you'll play a pivotal role in developing and enhancing our innovative supply chain solutions. You'll collaborate closely with a talented team of passionate engineers and industry experts, applying data engineering and data science techniques to optimize supply chain processes. Your expertise will directly contribute to providing our clients with real-time visibility, predictive analytics, and actionable insights.
What You Will Do
- Develop and implement machine learning models to improve the accuracy of supply chainETA s.
- Analyze large, complex datasets to extract meaningful insights and identify trends.
- Design, build, and maintain scalable data pipelines and data lakes to ensure efficient data flow and storage for analytics purposes.
- Collaborate with cross-functional teams to integrate analytics solutions into our products.
- Design and build data visualization tools to communicate findings to stakeholders.
- Continuously research and apply the latest techniques in AI and machine learning to supply chain problems.
- Contribute to the development of our analytics product suite, ensuring scalability and efficiency.
- Communicate complex data science concepts to non-technical stakeholders.
Key Responsibilities
1. Advanced Predictive Models: Utilize machine learning algorithms to analyze historical data and identify patterns. Models such as regression analysis, neural networks, or ensemble methods can predict ETAs more accurately by considering various factors like traffic conditions, weather, route efficiency, and historical performance.
2. Real-Time Data Integration: Incorporate real-time data into predictive models. This includes traffic updates, weather conditions, vehicle speed, and location data. Real-time data allows for dynamic adjustments to ETA predictions as conditions change.
3. Historical Data Analysis: Analyze historical data to understand common delays and their causes. This analysis can help in adjusting the predictive models to account for recurrent issues.
4. Route Optimization: Use data analytics to identify the most efficient routes. Optimized routing not only shortens travel time but also makes ETA predictions more reliable.
5. Machine Learning for Anomaly Detection: Implement machine learning algorithms to detect anomalies that could affect delivery times, such as unexpected traffic jams or vehicle breakdowns, and adjust ETAs accordingly.
6. Sensor Data Utilization: Leverage data from IoT devices and sensors equipped in transportation vehicles. This data can provide insights into vehicle performance, road conditions, and other factors that influence travel time.
7. Advanced Analytics Techniques: Employ advanced analytics techniques like time series forecasting, geospatial analysis, and simulation models to enhance the robustness of ETA predictions.
Skills, Knowledge and Expertise
We value diversity and encourage all interested candidates, regardless of whether they meet all qualifications, to apply. While the following qualifications are relevant to our work, they are not strict requirements:
- BS or MS degree in Data Science, Statistics, Computer Science, or a related field.
- Proven experience in machine learning, statistical modeling (5+ years)
- Proficiency in programming languages such as Python or R.
- Experience with SQL and working with large datasets.
- Strong expertise in designing and building robust data pipelines, data lakes, and optimizing data storage and retrieval structures.
- Knowledge of supply chain processes and logistics is a plus.
- Strong problem-solving and analytical skills.
- Excellent communication and teamwork abilities.
- Speaking Spanish is a plus!
- We prioritize Eastern Time Zone (NYC) and CET (Amsterdam) location base for employees so they can join regular team sessions. comfortably.
