Data Scientist

• [CANDIDATES WHO REQUIRE WORK PASSES NEED NOT APPLY]

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.

Key Accountabilities

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
  

Project Management

• 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

REQUIREMENTS

• 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 in data visualization tools or libraries such as Tableau, Power BI or packages in Python or Javascript
• 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