Dr. rer. nat. in Physics, Georg-August-University Goettingen, Germany (2014)
| Data Scientist | |
|---|---|
| causaLens | |
Year entered into a non-academic position: 2014
Job highlight: Fast-paced environment, direct impact, high flexibility in pursuing research interests.
My research training set me up to… Solve ill-defined problems, work independently and guide research directions, efficiently create high-quality deliverables (articles and software).
Left academia after: PhD
What’s your background?
I’m a physicist by training, having studied in Dresden, Bristol and Goettingen. After starting off as an experimental physicist working on thin conductive films, my advisor realised that I can program – then I drifted from writing data analysis software to computational physics.
Why did you move away from academia?
While academic work is very intellectually stimulating, I appreciated the more immediate impact that can be achieved by working in industry.
My current role provides the perfect combination of both aspects: I have the chance to build cutting edge time series prediction models based on the latest academic research while helping clients solve immediate business problems based on the output of these models.
Is there anything you miss about academia?
I slightly miss the opportunity to go on a 3-4 week long wild goose chase down an avenue of research that is at best academically relevant, but seems interesting at the time. Most roles in industry expect a slightly more focused approach – but, to be honest, pursuing a successful career in academia does as well.
How did you get this job? Did you face any challenges when considering a move away from academia or applying for the role?
This is my second job outside of academia, therefore the stigma of coming fresh from university without “practical” experience does not apply anymore. I would recommend pursuing 1-2 relevant internships during the studies or the PhD – just to indicate that you are able to follow directions and deliver results in a timely manner. Also, when applying after one or two post-docs, please make it clear why the position you are applying for is a logical continuation of your career – no one likes to hear “I didn’t cut it in academia, but your company seems like a sensible second choice”.
What motivated you to/why did you choose the sector you transitioned into?
In one of his essays, Paul Graham put it quite succinctly: Look for smart people and hard problems. Building better technology to predict the future is a hard problem and using it to inform the allocation of resources in our society is a way to create an immediate impact. More pragmatically: I chose it because I’m surrounded by people I can learn a lot from and I won’t get bored.
Did you think you had the skills required for your current position before you started? Were you right?
If you are starting a job where you think you have all the required skills, you’re not pushing hard enough. I’d aim for 70% match in skills and a high conviction that you can acquire the remaining 30% in the first 3-6 months.
How did your PhD prepare you for your current job? For example, what were the transferable skills that you developed during your PhD that are most relevant to your current job?
There are a few skills that I acquired over the course of my PhD which help me to this day. Most importantly, the ability to solve ill-defined problems – often the hardest (and most important) problem is which question to ask. I also learned to work independently and guide the direction research should take myself. The ability to efficiently create high-quality deliverables came in two stages: during the PhD I learned to create software and articles that fulfil the high requirements of academic rigour, and during my time in consulting I learned about the importance of delivering good output on faster-than-academic timescales.
Did you have any preconceptions about your sector that proved to be wrong?
I guess everyone has stereotypes about consultants, and everyone has stereotypes about startup employees – I also had mine. In both roles, I was in the fortunate situation to work in teams with smart people that continuously did their best to advance the projects. Whether they have a Tumi carry-on and an expensive watch or wear a company t-shirt and drink Kombucha from the company fridge is really irrelevant.
Can you describe a typical week in your job?
I haven’t really experienced a typical week yet. During one week, I might prepare and present a set of slides for a potential client, build a proof-of-concept to predict banana production (true story) one year ahead across the world, work with a colleague on a specification for a new feature on the platform, help a new colleague get up to speed with our platform and have 2-3 phone or in-person interviews. In a typical week, I will manage to take time for pizza and beer in the coworking space on Thursday evening, though.
What’s the workplace culture like? Please include comments on work-life balance, flexibility, remote working?
Our work culture is very intellectual and meritocratic – everyone will be heard and the best way to see your ideas realised is to write a pull request on github. We have a few bi-weekly reading clubs and we invite people with a relevant academic background to give talks on a regular basis. On the side, we have some sports activities going on where parts of the team participate, such as basketball, volleyball and bouldering. Overall, we try to maintain a good work-life balance and support remote work to a certain extent – one day of home office per week is no problem. No one would expect a startup job to be a 9-5 job with an hour lunch break per day, though.
Do people with a PhD frequently get hired in the company/sector?
Yes – our firm currently consists of 70% PhDs, most of them with a STEM background. This is a result of us working at the intersection of the deep-tech sector and financial services.
What are your favourite parts of your job?
I very much enjoy following a feature on the platform go from an idea to being part of a product being used and appreciated by our clients.
What are your reflections on your career path?
Whilst my career path was not quite straightforward, I think the diversity of my experiences prepared me very well for the role I’m currently in. Over time, I observed that most people – me included – are more risk-averse than objectively justified. I can only recommend: go out, try things – maybe you succeed, maybe you learn.
Do you have any advice for current graduate students and postdocs considering a career outside of academia?
Try to reach out to alumni of your research group or university. Most people are happy to give their perspective on the field they are working in and the path they took to get there. I would recommend preparing some more specific questions for them than “I’m looking for a career outside of academia, what should I do?”.
What do you know now that you wish you’d known when exploring a transition?
When exploring options after my PhD I did not understand that recruiting is a skill that you can hone like any other skill.
Learn more about the sector and the firm, read the recent news, check the relevant forums and learn about the standards for their interview process – thanks to the internet there is a lot of information available nowadays (but also to the other candidates).
Can you recommend any relevant resources, organisations or events that might help somebody new to the sector find out more about it?
Most sectors have professional associations organising events and bringing professionals together, such as the CFA institute or GARP for asset management and risk management. Joining for their events gives you insights into current topics in the field and a chance to talk with practitioners about careers in the sector.
causaLens is a deep-tech company on a mission to optimize the global economy. We’re building the next generation of predictive technology for complex and dynamic systems with HQ in London and presence in key global markets.
causaLens is named as one of the most disruptive and promising UK companies in Artificial Intelligence. We’ve been selected as a Best Investment in DeepTech 2019 by UK Business Angels Association.
The team consists of top scientists and engineers whose previous employers include prominent hedge funds and research organizations. More than 70% of our team holds PhDs from top universities all around the world. causaLens is supported by well-regarded institutional VC investors.
Our target customers are businesses in various industries, that focus on dynamic processes and would benefit from additional input, that would help them to make better decisions with a future impact. causaLens predictive technology currently helps various businesses in decision making in the following industries – banking and finance, transport and logistics, commodities, and others.
Our values and principles are:
– Scientific rigour
– Ambition and pushing boundaries
– Trustworthiness and ownership
– Kindness and inspiring others
– Customer drive
causaLens in the news
“Meet causaLens, a Predictive AI For Hedge Funds, Banks, Tech Companies” – Yahoo Finance [Link]
causaLens in “The U.K.’s Most Exciting AI Startups Race To Scale” [Link]
causaLens’ Auto ML Platform Draws Interest from Discretionary Funds [Link]
causaLens wins the “Best Investment in Deeptech” award by the UK Business Angels Association awards [Link]
causaLens in the 100 most disruptive UK companies [Link]
Financial Times – ‘AllianzGI taps virtual data scientists amid war for talent’ [Link]
Forbes – ‘Machine Learning Companies to watch in Europe’ [Link]
causaLens Appoints Hedge Fund Veteran and Data Leaders to Advisory Board [Link]
We offer internship opportunities and we have a 100% intern conversion rate to full-time employees (for graduates).
