Lead data scientist team of the Business Transformation Office and provide technical leadership in data modelling, machine learning, validate technical feasibility of data use cases and determine technical scope.
Provide analytics-based thought leadership across a variety of technical and non-technical audiences to ensure that all levels of GE make data-driven decisions.
Subject matter expert to other data scientists, data engineers and analysts across the company as an available resource for all things related to analytics.
Develop a machine learning/deep learning models for propensity modeling, user segmentation, text analytics, video analytics, churn prediction, personalisation/recommendation
Tell stories that describe analytical results and insights in meetings of all sizes with diverse audiences
Drive a framework and leadership on how Singtel GE work with internal and third-party data (think Redshift, Python, Tableau, R) to make strategic recommendations (e.g., personalized user flows, segmented marketing audiences, more accurate recommendations, churn prediction, propensity models).
A degree in Computer Science, Applied Math, Statistics, or Industrial Engineering.
A relevant Master’s degree (or higher) is highly regarded and can be used in place of some work experience
6+ years of work experience involving quantitative data analysis and complex problem solving.
Complete command of SQL, and either Python, R, or Hadoop frameworks along with some experience with Tableau. Proficiency with similar BI and visualization tools is also transferable.
Extensive experience directly querying large data sets including networks data, customer usage data, third party data and raw data ingested from non-standard platforms.
Strong written, verbal, and visual communication skills to concisely communicate in a way that provides context, offers insights, and minimizes misinterpretation.
The skills to work cross-functionally and push business partners to focus on realistic goals and projects.
Experience with distributed analytic processing technologies (think Hive, Spark)
Experience building and deploying analytic solutions as well as machine learning and/or optimization models in production.
Solid statistical knowledge and analytic thinking, ideally utilized in modelling and experimentation.
Managing and motivating a team of technical specialists who are data scientists.