[keppel Internship Programme 2023] Intern, Data

3 days ago

Singapore Keppel Corporation Full time

Keppel Land is the property arm of Keppel Corporation, one of Singapore's flagship multinational companies with a global footprint in more than 20 countries. As a multi-faceted property company, Keppel Land provides innovative real estate solutions with its sterling portfolio of award-winning residential developments, investment-grade commercial properties and integrated townships.

As part of the Digital & Technology team, the Data Scientist Intern will work within a data science team to generate understanding, strategy and guide key decision making based on data.

The intern will be responsible in the following:

- Transform large, complex datasets into pragmatic, actionable insights
- Identify, source and organize data required for the solution and prepare it in a way that makes it fit for use
- Use data visualization skills and storytelling to drive actions from stakeholders
- Design and interpret quantitative experiments to objectively guide key business decision making
- Have the capacity to adapt quickly and take ownership of your work

Learning Outcomes:
1. Transform complex data sets into pragmatic insights which you provide through skilled storytelling.

2. Combine sound business understanding, data handling, programming, statistical modeling and data visualization to derive actionable information from broad sets of data while valuing pragmatism over perfection.

JOB REQUIREMENTS
- Undergraduate pursuing a degree in Computer Science, Computer Engineering, Statistics or Information Technology
- Experience solving real problems using data mining techniques and with statistical rigor
- Strong technical skills regarding data analysis, statistics, machine learning and programming. Strong working knowledge of Spark or SQL as well as coding experience with Python and/or R
- Strong skills in translating business problems into data problems
- Strong data visualization skills
- Strong knowledge of Statistical Methods (e.g. Hypothesis Testing, GLM, Causal Inference, Forecasting)
- Strong knowledge of Statistical/Machine Learning (e.g. Dimension Reduction, Clustering, Regularization, Bagged/Boosted methods)
- Knowledge of data management (engineering, data pipelines, workflows)
- Strong knowledge of the possibilities and limitations of different types of data analysis and/or data science modeling

BUSINESS UNIT

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