The importance of creating visualizations, like the F1 race trace, for practical comprehension in complex problem-solving scenarios.
Validating data value before significant investments is crucial, as highlighted by a pivot to the transportation sector for data accuracy.
Accommodating neurodiversity in the workplace, such as understanding feedback preferences and minimizing interruptions, improves team dynamics and performance.
Deep dives
Building a Team and Finding Value in Alternative Data
Mark discusses his career path, blending psychology and operations research, and highlights his experiences at companies like McLaren. He mentions the significance of creating visualizations like the F1 race trace and emphasizes the importance of practical comprehension in complex problem-solving scenarios.
Transition to Telefónica and Lessons Learned
Mark shares his transition to Telefónica's Smart Steps division, delving into the challenges faced with data precision and value creation. He reflects on the failure of initial insights product for retailers due to unreliable data, leading to a pivot towards the transportation sector where data accuracy was essential. He underscores the importance of validating data value before significant investments.
Shifting to Schroders and Utilizing Alternative Data
Mark narrates Schroders' adoption of alternative data in 2014 and the establishment of the data insights team. He describes the team's growth and focus on serving various investment teams within Schroders using data science techniques. Mark elucidates the team's approach of answering questions and providing unique insights tailored to specific investment philosophies, contrasting the approach with hedge funds' strategies.
Adapting During the Pandemic with Insightful Webinars
During the pandemic, Mark reveals the team's shift towards analyzing COVID-19 related data, responding to colleagues' inquiries and facilitating biweekly webinars. He explains how the team's focus on understanding the underlying biology of the virus differed from traditional economic analysis. Mark highlights the value provided by synthesizing vast amounts of COVID-19 information and addressing colleagues' questions effectively.
Utilizing Data Science for Investment Success
Investing in data science was attributed to the success of funds outperforming benchmarks in the 2021 annual results. The key takeaway was the importance of using a meta machinery to understand investor preferences and blind spots, driving the discovery of valuable insights.
Impact of Autism and ADHD in the Workplace
The discussion on neurodiversity highlighted the significance of understanding and accommodating individuals with autism and ADHD in the workplace. Acknowledging feedback preferences and minimizing interruptions benefitted team members, leading to improved work dynamics and performance. Coding and agile software development were identified as environments conducive to the strengths of individuals with autism.
In this episode I speak to Mark Ainsworth, the former head of Data Science at Schroders, a two-and-a-half-centuries-old asset manager that is notable for its use of alternative data.
In this episode, Mark and I discuss what made Schroders take this unusual route, and how his work there differed from the more familiar alternative data working patterns of hedge funds.
We also touch on a subject dear to Mark’s heart, which is neurodiversity and its higher propensity amongst technologists.
DISCLAIMER
This podcast is an edited recording of an interview with Mark Ainsworth recorded in June 2023. The views and opinions expressed in this interview are those of Mark Ainsworth and Mark Fleming-Williams and do not necessarily reflect the official policy or position of either CFM or any of its affiliates. The information provided herein is general information only and does not constitute investment or other advice. Any statements regarding market events, future events or other similar statements constitute only subjective views, are based upon expectations or beliefs, involve inherent risks and uncertainties and should therefore not be relied on. Future evidence and actual results could differ materially from those set forth, contemplated by or underlying these statements. In light of these risks and uncertainties, there can be no assurance that these statements are or will prove to be accurate or complete in any way.
