Main accountabilities:

• Understand the business problems and how to deliver relevant insights that lead to actions


• Gather data and build data-science and algorithmic solutions to address business problems requiring descriptive, diagnostic, predictive, and / or prescriptive analytics


• Create experimentation to solve complex business problems and deliver predictions on future business outcomes in a repeatable and relevant way


• Support product teams scaling models into low-touch solutions to provide optimal ROI from data science


• Work on in-market business problems coming from CCBTs, CD, Finance and CLT. Identify common themes to build repeatable models and partner with CD, CCBT, Finance leadership to support 4G growth though data and analytics, and / or advance the next phase of NRM


• Position analytics as a tradable “currency” with customers to gain competitive advantage


• Drive new value from insights from connecting external and internal data sources


• Innovate new data and analytic methodologies driven by local needs which feed into D&A’s global product pipeline

EXPERIENCE, QUALIFICATION AND QUALITIES:

Essential

• B.S. or M.S. in a relevant technical field (Operations Research, Computer Science, Statistics, Engineering, or Mathematics)


• 1-4+ years’ work experience in a data science role with significant focus on large scale and/or unstructured data


• Experience managing projects from start to finish


• Passion for empirical research and for answering hard questions with data


• Ability to apply an agile analytic approach that allows for results at varying levels of precision


• Strong communication skills, particularly the ability to communicate complex quantitative insights in a precise, and actionable manner to business leaders


• Strong track record in solving analytical problems using quantitative and machine learning approaches


• Working knowledge in common machine learning techniques such as Random Forests, Boosting, Regularized Regression, Naïve Bayes Classifiers


• Working knowledge of advanced machine learning such as Deep Neural Networks, Support vector machines, Reinforcement learning and Bayesian networks


• Working knowledge in classical statistics (Regression, Clustering, Optimization, Time Series, Probability)


• Deep experience in testing and measurement (A/B, multivariate, inferential measurement e.g. CausalImpact)


• Deep experience working with and coding in R, R Shiny, Python


• Working knowledge of data visualization concepts in reports (Power BI) and specialist tools (D3 or equivalent)


• Knowledge of extracting and combining complex, high-volume, high-dimensionality data from multiple sources (enterprise, proprietary, IoT, public domain), including unstructured data (comment threads, audio, video)


• Working knowledge working with large data sets, experience working with distributed computing tools a plus (Apache Spark, Hive, Impala)


• Working knowledge working in Microsoft Azure and scaling analytic products over GPUs in the cloud


• Self-directed


• Possesses a natural curiosity, openness to possibilities and imagination to create novel business solutions


• Ability to work collaboratively with peers and demonstrate vertical and lateral influence.

Professional

• Machine learning forecasting techniques Fully Operational


• Statistical modelling Fully Operational


• Operational research and supply chain Working Knowledge


• Optimisation techniques and tools Fully Operational


• Manipulating multi-source data Fully Operational


• Python coding Fully Operational


• Cloud architecture (preferably MS Azure) Working Knowledge


• Simulation packages e.g. AnylogicWorking Knowledge


• Distributed computing (Hadoop, Spark)Working Knowledge

General Skills

• Project Management Working Knowledge


• Communication / presentation skills Working Knowledge


• Agile methodology Working Knowledge

Standards of Leadership

• Personal Mastery (Data-science and advanced analytics)


• Agility


• Passion for High Performance


• Business Acumen

KEY CAPABILITIES:

• Business Acumen


• Data Science


• Machine Learning


• Programming


• Statistical Analysis


• Data Analytics


• Data Expertise


• NLP / NLG


• Model re-engineering


• Model Deployment


• Machine Learning


• Python


• Cloud programming


• Data warehouse, Data lakes