Agency: National Environment Agency
Formed on 1 July 2002, the National Environment Agency (NEA) is the leading public organization responsible for improving and sustaining a clean and green environment in Singapore.
The NEA develops and spearheads environmental initiatives and programmes through its partnership with the People, Public and Private sectors. It is committed to motivating every individual to take up environmental ownership and to care for the environment as a way of life.
By protecting Singapore's resources from pollution, maintaining a high level of public health and providing timely meteorological information, the NEA endeavours to ensure sustainable development and a quality living environment for present and future generations.
Executive Manager/Manager/Assistant Manager (Business Intelligence Branch)
We are looking for a highly self-motivated individual to join the Business Intelligence (BI) Team, who have passion for discovering solutions hidden in large data sets and working with stakeholders to improve the many business domains that NEA offers. In this role, you would also be involved in inter-departmental projects, and be responsible for the following:
- Play a leading role in the Prototyping, Application and Creation of Prescriptive, Predictive and Analytical dashboards and models to support various departments and glean insights from data to make better data-driven decisions. These include (but are not limited to):
- Identify Opportunities for Prescriptive, Predictive and Analytical models for NEA to improve operations deployment in the field and decision making at the operations level.
- Conceptualise/ Apply/ Prototype new/ existing data models for use in NEA
- Pilot the implementation of such models with departments to support their line of work
- Operationalise data models (including change management)
- Provide maintenance support (level 1) for developed models to various departments
- Support the usage of technology (e.g. R, Python) to automate data processing and ETL (Extract, Transform and Load) for departments
- Understand and work closely with stakeholders to implement innovative and new ways of working (e.g. processes, organisation structures, systems) and leveraging on BI in their work
- Support in establishing and maintaining Data Structure and Data Governance Framework for BIS
- Support enhancements to *NEA Business Intelligence System (BIS). These include (but are not limited to):
- Gather and understand business requirements from various departments
- Design and prototype Dashboards
- Operationalise Dashboards (including change management) with appointed vendor
- Provide basic maintenance support for dashboards to various departments
- Support the development and implementation of outreach programmes to keep NEA staff informed of transformation efforts and feedback channels while encouraging and soliciting ideas from NEA officers
*Note: BIS is a dynamic business intelligence and analysis platform that provides an integrated view of cross-functional information across NEA.
Job Requirements:
- Degree in Statistics, Mathematics, Computer Science or related field.
- At least 3-5 years of experience in business analysis, data sets preparation and building statistical models.
- Highly self-motivated individual who is mature, independent and resourceful with a drive to learn and master new technologies and techniques.
- Good written and verbal communication skills for coordinating across teams.
- Prior knowledge or experience in implementing the use of BI visualisation tools (e.g. Qlik Sense, PowerBI, Tableau).
- Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.).
- Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Prior knowledge or experience in the following would be an advantage:
- In the field of business process re-engineering (including tools like Lean Six Sigma) and/or systems
- Using statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets
- One or more of the following languages such as C, C++, Java, JavaScript, etc
- Statistical and data mining techniques such as GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc
- Querying databases
- Working with and creating data architectures
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