As the innovation leader in the logistics industry, Deutsche Post DHL Group (DPDHL) focuses on output-oriented innovation management. DHL Asia Pacific Innovation Center (APIC) drives trend research and development of innovative and future-ready supply chain solutions in addition to serve as a regional platform for collaborative innovation with customers, industry partners, research institutes and academia.
Formed in 2016, DHL Applied Analytics is a part of APIC and aims to spearhead the design and development of innovative customer-centric analytics solutions towards the development of new business concepts and value-add across the supply chain.
If you are a dynamic team player with an entrepreneurial spirit to make a change with your skill set and potential, we’d like to welcome you to explore the career opportunities available with DHL Applied Analytics.
Analytics Solution Design and Development
- Design and implement innovative solutions using statistical models, machine learning and other data mining techniques
- Handle challenges of analysing real-world data, such as incompleteness, bias, class imbalance and high volatility
- Create effective visualizations and dashboards to communicate insights with end-users
- Support the development and deployment of scalable and robust software solutions, working with IT solutions engineers
- Engage with customer management teams to understand business requirements, project scope and develop a sound project plan
- Think strategically and creatively, to exploit data science for a competitive advantage
- Manage project implementation (for analytics scope only) to ensure that solutions are delivered on time and on requirements
- Knowledge Development and Management
- Research and experiment with new advances in analytical methods, algorithms and tools to deepen and develop new capabilities (i.e. deep learning, optimization, parallel computing)
- Elevate team competencies by sharing the latest trends and best practices
- Support knowledge management by ensuring that learnings and insights arising from conferences, projects and industry engagements are documented
- 2-5 years experience in Data Science, Analytics
- Experience with Python, including packages such as NumPy, Pandas and scikit-learn
- Experience in training, selecting and tuning machine learning models, employing techniques like data preparation, feature engineering, cross-validation and bagging/ boosting.
- Experience with RDBMS such as MySQL, PostgreSQL, MongoDB, MS SQL Server
- Good verbal and written communication skills
- Good project management and organization skills
- Enjoy self-direction, self-learning and creativity
- Good team player
Nice to have:
- Experience in developing NLP (Natural Language Processing) and image recognition models
- Experience with deployment and maintenance of models in production, e.g. APIs with capped response time, performance monitoring, automatic retraining and tracking data/ model lineage
- Experience with cloud platforms like Azure or GCP, especially in relation to data science
- Experience with Docker, Kubernetes and other supporting container platforms/ tools
- Experience in data crawling including popular social APIs and web scraping
- Experience with application development, particularly full stack web development
- Experience with Hadoop