General Job Description
The Innovation and Transformation Office (ITO) of Global Safety Division (GSD) leads disruptive idea generation, research, testing and prototyping with the goal of launching breakthrough products and services inspired by global trends and emerging technologies, contributing to a Safer City. We aim to take our deep technical and product expertise and develop innovative solutions that helps to transform businesses.
Looking a passionate and technical leader who enjoys technical challenges and possess excellent communication skills. Someone who has a strong start-up mentality and experience, as well as proven track record within a cross-cultural multi-national corporation, delivering large mission critical solutions in the area of data science and machine learning.
This role will have to work closely with customers / partners to produce innovative and actionable quantitative models and analysis to address the challenges in the areas of Public Safety.
The Innovation and Transformation Office (ITO) team is a catalysing force that crystalizes visionary concepts into proof-of-concepts (POCs) and prototypes that will bring real value to organizations. These solutions need to be scalable to support millions of customers worldwide.
The Successful Applicant
- Minimum Bachelor’s degree in Computer Science or Data Science (preferred). Or,
- Bachelor’s degree in any of Statistics/Mathematics/Technology plus a certification or rich experience in Machine Learning/Deep Learning/ETL.
- Minimum of 5 years of quantitative analytics experience with a focus on statistical modeling, Machine Learning, forecasting, optimization and/or predictive analytics.
- Experience in biometrics and facial recognition technologies is a plus.
- Must have strong programming experience with Python and R.
- Experience in machine learning, deep learning, data visualization, statistical, text analytics libraries, jupyter notebook and/or frameworks in Python or R.
- Experience with data processing and data analytics.
- Experience in predictive modelling algorithms, supervised and unsupervised learning methods, building statistical models (regression and neural networks).
- Experience in scikit-learn, numpy, pandas, seasborn, matplotlib, ggplot, deep learning framework: tensorflow, keras or pytorch.
- Experience in public cloud infrastructure such as AWS and/or Google Cloud Platform for high performance computing.
- Experience in developing and deploying applications running on public cloud infrastructure.
- Experience in Git for code management.
- Excellent written and verbal communication skills for coordinating across teams.
- Demonstrated experience applying a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
- Experience visualizing/presenting data for stakeholders using: Tableau, D3, ggplot is a plus.
- Experience in performing distributed data analysis on large data set will be an added advantage.
- Drive to learn and master new technologies and techniques.
Design of cloud environments with focus on AWS, and Azure for enterprise-level initiative
Provide leadership in the areas of data analytics and modelling with a strong focus on Public Safety. Develop and deploy AI technologies for public safety.
Manage machine learning projects, including writing functional and program specifications and documentation, data (structured and unstructured) acquisition from external and internal sources, data preparation (data cleaning, data mapping, data quantity and quality validation), identifying suitable machine learning algorithms to apply on the data sets, building machine learning model from the data, and tuning model parameters for enhanced performance.
Develop processes and tools for evaluating the performance of machine learning algorithms and robustness of the models. Establish specific success criteria for selecting the best machine learning model to address a real-world problem. Perform system testing and user acceptance testing to validate the robustness and performance of the machine learning models.
Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.
Design, build, test, validate, and deploy statistical and machine learning models to answer business needs and increase operational efficiency.
Develop custom data models and algorithms to apply to data sets.
Develop processes and tools to monitor and analyze model performance and data accuracy.
Actively engage in the creation of new disruptive and transformational products and services through an understanding of the end user’s requirements and operating environment.
Participate in the building and enhancement of a robust pipeline to support the automation of various development associated tasks to achieve continuous integration and delivery.
Create Intellectual Property (IP) in the form of patents, publication of papers in the relevant areas that is tightly aligned to the strategic direction of the GSD.