Research Fellow - Data Science and Marketing AnalyticsJob No.: 648875 Location: Clayton campus Employment Type: Full-time Duration: 18-month fixed-term appointment Remuneration: $75,115 - $101,944 pa Level A (plus 17% employer superannuation)
At Monash, work feels different. There’s a sense of belonging, from contributing to something groundbreaking – a place where great things happen. We value difference and diversity, and welcome and celebrate everyone's contributions, lived experience and expertise. That’s why we champion an inclusive and respectful workplace culture where everyone is supported to succeed. Together with our commitment to academic freedom, you will have access to quality research facilities, infrastructure, world class teaching spaces, and international collaboration opportunities. The Opportunity The Department of Marketing is one of the largest providers of tertiary level marketing education in Australia. The Department also has a strong research profile and a vibrant research culture. We are seeking a Level A research-only academic who will be expected to contribute towards the research effort of the University and to develop their research expertise through the pursuit of defined project relevant to the particular field of research. This project will work with Professors Stephan Ludwig, Peter Danaher, Lan Du and Yu-Ting Lin in a post-doctoral Research Fellow position in data science and marketing analytics. The project involves using machine/deep learning and natural language processing to develop a combination of topic and transformer models to automatically summarise the sentiments (e.g. Trust) towards marketing relevant aspects (e.g. Service) as they are mentioned in the texts of online customer reviews. It also involves overseeing a large pre-coding exercise of various customer review databases. The post-doctoral fellow will be responsible for the development and implementation of the methodology, the creation of an easy-to-use web application, along with its empirical application, under the guidance of the expert supervisory team. Professors Stephan Ludwig and Peter Danaher are both located at the Caulfield campus of the Monash Business School. They are leading marketing researchers, with a particular focus on NLP, marketing modelling and analytics. Dr Lan Du is a senior lecturer in Data Science and AI in the Faculty of IT, Monash University. His research interest lies in the joint area of machine/deep learning and natural language processing and their applications in different domains. Dr Yu-Ting Lin is a Lecturer in School of Marketing at The University of New South Wales. Her research interest lies at the intersections of the human mind and digital technology including emotions, data-driven marketing and service innovation. The position will allow the individual to challenge themselves and reach their full potential whilst working alongside other motivated team members. So, if you’re looking for the next chapter in your career, it’s here. As the successful candidate you will be responsible for:
The successful appointee will have a PhD (or near completion) in data science, mathematics or econometrics, with experience in applied algorithm development and advanced programming skills. Diversity is one of our greatest strengths at Monash. We encourage applications from First Nations people, culturally and linguistically diverse people, people with disabilities, neurodiverse people, and people of all genders, sexualities, and age groups. Be part of our story. Work with us to #ChangeIt. Monash avidly supports flexible and hybrid working arrangements. We have a range of policies in place enabling staff to combine work and personal commitments more easily. At Monash University, we are committed to being a Child Safe organisation. This position at the University will require the incumbent to hold a valid Working with Children Check. Your application must address the selection criteria. Please refer to Enquiries Professor Stephan Ludwig, Professor, Faculty of Business and Economics, stephan.ludwig@monash.edu Position Description
Closing Date Monday 27 March 2023, 11:55pm AEDT
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