The presentation information for WQF Americas will be added soon.
PANEL: Talent Attraction & Retention
- What are QR Financial Services currently doing and what should they be doing to attract more female talent?
- What can Universities and Recruitment companies do to help?
- What strategies are financial companies using at present if any?
- What are QR Financial Services currently doing and what should they be doing to retain female talent?
- What top positions besides Asset Management can QF- profiled women occupy?
- For each position open, the percentage of female CVs submitted is very small (if not none). Why is this happening and how can universities/headhunters/companies work together to improve the numbers?
- At more senior levels the number of women is even lower than at entry level which means that the female population retention rate is low or/and women are not being promoted. Discuss.
- Mentoring programmes that could specifically help Diversity & Inclusion.
- Are quantitative positions too specialised which prevents women (and men) to move horizontally to different (and possibly more senior) roles?
PANEL: Machine Learning, AI & Quantum Computing in Quantitative Finance
- What is the current state of utilisation of machine learning in finance?
- What are the distinct features of machine learning problems in finance compared to other industries?
- What are the best practices to overcome these difficulties?
- What’s the evolution of a team using machine learning in terms of day to day operations?
- What is a typical front office ‘Quant’ skillset going to look like in three to five years time?
- How do we deal with model risk in machine learning case?
- How is machine learning expected to be regulated?
- What applications can you list among its successes?
- How much value is it adding over and above the “classical” techniques such as linear regression, convex optimisation, etc.?
- Do you see high-performance computing (HPC) as a major enabler of machine learning?
- What advances in HPC have caused the most progress?
- What do you see as the most important machine learning techniques for the future?
- What are the main pitfalls of using Machine Learning currently in trading strategies?
- What new insights can Machine Learning offer into the analysis of financial time series?
- Discuss the potential of Deep Learning in algorithmic trading?
- Do you think machine learning and HPC will transform finance 5-10 years from now?
- If so, how do you envisage this transformation?
- Can you anticipate any pitfalls that we should watch out for.
- Discuss quantum computing in quant finance:
- Future uses
PANEL: Career Progression
- Do you think that being a woman is a significant factor in slowing down career progression in QR Financial Services?
- If so, could this be avoided and how?
- Discuss the Importance and value of mentorship and sponsorship
- What mentoring programs are available for juniors if any?
- Is it still hard to make it to the top positions, if so why and what can do done to change the situation?
- Discuss female role models in finance and significant achievements
- Tips from coaches on career progression (eg having your voice heard)
- Actively managing your career; distribution of opportunity set
- Gender diversity issues (discuss numbers, policies, how to address it)
- Maternity leave
- How important are the following:
- Promotions/Career opportunities
- Pay gap elimination
- Agile/Flexible working
- Getting the feedback you need (even if you don’t really want it)
- Supporting each other