Next Best Action Recommendations Based on Large Scale Data Analytics, User Interactions and Social Rules within an Enterprise Environment
Location: Sydney, NSW
Duration: 5 months
Proposed start date: ASAP
The world continues to become automated and software and hardware are becoming more intelligent. Many of the tasks we perform in our daily work are being taken over by technology. However, human relationships remains a key point of difference that AI cannot replicate. We believe that in the future your network will be one of the most important factors that both differentiates you and allows you to transact. Your network will last the test of time and protect you.
We also believe that selling good is an integral part of human trade which is an essential part of increasing economic wealth. FlightSpeed looks to use advanced data analytics to help untrained or uneducated people to master the art of selling. Or well trained people, do an even better job of relationship management. This may greatly benefit people in emerging economies around the world.
We are seeking a PhD intern with expertise in data analytics, machine learning, software development, recommendation system or related fields to help us enhance our proprietary algorithm and to map out how users make the choice of the next best action to enhance a relationship.
Research to be Conducted
FlightSpeed software allows users to access a vast amount of information. However, users face a challenge in deciding the next best action (“NBA”) to advance a commercial relationship.
How can we formulate a NBA recommendation algorithm that utilises global data from an organisation, user behaviour (i.e. their previous interactions with the target) and the actions that others have taken previously with the target (i.e. other salespeople interacting with the target). How can an organisation act like an organism and interact with another organisation to better the relationship for mutual gain. How do we augment this algorithm with social rules to create guardrails to ensure a user does not transgress social norms?
We are looking for a PhD student with the following skills:
- Mathematics, machine learning techniques, and code in Python or R
- Experience on large-scale analysis techniques since they will need to handle large amounts of data
- Review and analyse significant amounts of data from a variety of sources to create an independent model of human behaviour in relationship building.
- Provide an algorithm recommends a NBA at each phase of the relationship building process. The ultimate goal of the algorithm should allow FlightSpeed to recommend the most appropriate NBA that is relevant to the user and also relevant to the target through the product’s mobile application.
- Method and tools to monitor and assess the performance of the algorithm in terms of ‘usefulness’ of the recommended NBA in terms of advancing the relationship quality.
- The model will be a stand-alone prototype, that uses the agreed data source, so that FlightSpeed can integrate the solution into our product.
- The Intern will support the FlightSpeed team to conduct user testing with the prototype and solicit feedback. This is so we can validate the algorithm.
If the prototype above is proved to be successful as judged by statistical testings and user feedback, we will put the feature on the product road map to have it integrated within the product.
The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.
It is expected that the intern will primarily undertake this research project during regular business hours, spending at least 80% of their time on-site with the industry partner. The intern will be expected to maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.
The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.
3 April 2019
APR – 0754