About Soledad
Soledad Galli is a data scientist, instructor, and software developer with more than 10 years of experience in world-class academic institutions and renowned businesses. She has developed and put into production machine learning models to assess insurance claims, credit risk, and prevent fraud. She teaches multiple online courses on machine learning, which have enrolled 45,000+ students worldwide and consistently receive good student reviews. She is also the developer and maintainer of the open-source Python library Feature-engine, which is currently downloaded 100k+ times per month. Soledad received a Data Science Leaders’ award in 2018 and was recognized as one of LinkedIn’s voices in data science and analytics in 2019. She is passionate about sharing her machine learning knowledge. She gives talks at data science meetings and writes articles about data science and machine learning.
Relevant links:
Train in data: https://www.trainindata.com/
Feature-engine: https://feature-engine.readthedocs.io/
How did you start out in your tech career?
I am a self-taught data scientist. I worked in academia as a research scientist for many years. When I decided that I wanted to leave academia, I looked at what I’d like to do next, and I discovered data science. Data science has one of the things that I like doing most: data analysis and interpretation. Next, I found out what skills I needed to become a data scientist. I knew statistics from my work at the university, but I still needed to learn how to program in Python and R from scratch and also machine learning. I was recommended a few online courses. So I took them, and that is how I started. It was both life-changing and mind-changing. After a few months of self-training, I landed my first job as a data scientist at a consulting company.
What are the signs of success in your field?
In terms of career progression, I’d say the same as in many other jobs: higher salaries and more responsibility, for example, by being promoted to data science lead and then heading a team of data scientists.
In terms of success at the task itself, depending on the company you work, a sign of success could be putting the first models into production, or if the company is already tech-savvy, then streamlining and optimizing current processes and improving the predictive models or data related products would be signs of success.
More rewarding for me, though, is seeing the fruits of using the data-related products that we build, like, for example, being able to detect fraud more effectively, or being able to help customers more efficiently thanks to predictive modelling.
In terms of personal growth as a data scientist, a sign of success is probably gaining experience and knowledge in the domain, and being acknowledged as an expert, for example, by being invited to give talks.
What is the best and worst thing about your job role?
One of the best things about being an instructor in data science is that I am constantly learning new things. Another wonderful thing is the reward of hearing from students about how my courses have helped them master a skill, grow in their careers, or nail some interview questions. The things that I like the least are those related to administration.
What can you advise someone just starting out to be successful?
I think the key is to enjoy what you do and to work in a healthy and rewarding environment where you can learn, for example, from your peers and support each other. I’d also advise you to continue learning and growing as a professional in any direction that you’d like to take it, be it purely technical or more on the management side.
How do you switch off?
I like cooking, dancing, listening to music, and with more time, hiking and cycling.
What advice would you give your younger self?
I guess the advice would be: If you are not happy where you are, just look for something different. If you don’t like what you do or the environment you are working in, just look for something else. There are plenty of opportunities to work on something more rewarding, surrounded by interesting and exciting people. I personally spent many years in academia, and I wasn’t always happy. I kept telling myself that it would get better and that I just needed to stick with it and endure it because if I left, I’d regret it. And the only thing I regret right now is that I did not leave academia sooner. So, the advice would be to check in with how you are feeling, and re-adjust, or not, accordingly.
What is next for you?
I’d be creating more courses on intermediate and advanced machine learning topics, and after that, I’d like to dive into creating courses for MLOps.
What are your top 5 predictions in tech for the next 5 years?
I’ll keep my predictions to my work field, which is tech education and open source. I think that as we learn more about how we can use data science in the industry and establish more common and best practices, there will be more open-source tools, courses, and books to spread the word about them. So I expect to see more and more people developing and maintaining open-source software, for example, and creating supporting resources around it.
I think there is also a great appetite for automation, so I expect to see more tools that consolidate some automation processes for machine learning model development. And I also expect to see more tools that bring the ecosystem of different existing tools together to facilitate their implementation and adoption by the wider community.
Watch Soledad’s session at the Data Science Festival below:
Feature Selection for Machine Learning
Data Pre-processing and Feature Engineering for Machine Learning
Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!
Want to become a DSF Speaker? Apply here!