DHL

Data Analyst, Finance, Customer Solutions and Inno...

Job description

Customer Solutions and Innovation (CSI) is a DHL's cross divisional commercial and innovation unit. We are responsible for managing DHL's largest and most strategically important customers. In a very competitive market, CSI work with our Business Units colleagues and provide value beyond what the customers expect, building stronger ties with Customer key decision makers. We focus on understanding our customer business and industry challenges and applying proven DHL solutions.

Role Purpose:

  • Support Group & CSI customer portfolio profiling
  • Manage Customer account hierarchy database cross BUs used for both financial and operational reporting.
  • Support & Enhance In house develop Python solutions
  • Data Preparation (Data cleansing, Data validation & Analysis)
  • Perform Data validation on web scraping & procured raw data.
  • Identify, collect and maintain financial and operational reference data.

Job Description

  • Data engineering support to in-house developed solution (Python, Kubeflow, Docker, API & ML)
  • Enhancements to existing solution using statistical models, machine learning and other data mining techniques
  • Research and experiment with new advances in analytical methods, algorithms and tools to deepen and develop new capabilities
  • Supporting customer account mapping and cleanup for specific BUs by CSI Customer and Industry management
  • Support CSI industry, Commercial & Operations with customer profiling to build additional hierarchy / layer (division/program etc) for better customer management and analysis.
  • Support CSI Finance teams with identification of customer revenue and other reporting KPIs
  • Support administrative processes around CSI customers going in/out and around mergers and acquisitions as well
  • Identify, collect and maintain financial and operational reference data used by CSI community which includes:
  • customer data: account numbers, group codes, industry/segment groupings and names, trading names, legal entities, subsidiaries, joint ventures, mergers and acquisitions
  • other relevant reference data
  • Proficiently use a variety of database, reporting and analysis systems made available by CSI and Business Units
  • Acquire, maintain and document functional knowledge and expertise on the global, regional & local DHL product portfolio and organisation, financial, operational and market intelligence

Education / Qualification / Certification / Requirements:

  • Bachelor's degree or equivalent experience in quantitative field: Statistics, Mathematics, Computer Science, Engineering, Data Science or Analytics
  • 1-3 years of experience in a data science/analytics role is preferred. Domain knowledge of finance is a plus
  • Fresh graduates with relevance internship(s) are welcomed to apply
  • Experience with Python, including packages such as NumPy, Pandas, scikit-learn, Keras, catboost, etc.
  • Experience in Data cleansing, Data validation and Modelling (Training, selecting and tuning machine learning models)
  • Experience in data visualization tools or libraries such as Power BI or packages in Python
  • Experience with RDBMS such as MS SQL Server
  • Excellent communication skills. Able to interact and communicate with all levels of staff and management
  • Possess excellent analytical, organizational, and interpersonal skills
  • Ability to work well under pressure, perform and manage multiple tasks simultaneously and meet dateline
  • Ability to identify issues and trends
  • Ability to interact with all levels of internal personnel on issues of major significance, often requiring coordination across different sectors and business units
  • Good written and process documentation skill
  • Good project management and organization skills
  • Curious and self-driven for new ideas

Bonus to have:
  • Experience in developing NLP (Natural Language Processing) and text matching
  • 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, 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

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