Omdena collaborators offer effective and fun data science mentoring where members can ask for career mentoring, technical mentoring, or personal development.
Depending on your background and the areas you wish to work on, you are matched with a mentor from the Omdena network. Together we set objectives and meet regularly to discuss progress and exchange ideas.
As Omdena collaborators are so diverse, individuals seeking mentoring can be students and recent graduates, but also professionals aiming to break into data science after careers in other fields.
For this article, we are interviewing Susanne, who, after a career as an engineer and project manager in the events industry, was looking to switch to the technology sector, and Galina, who was her career mentor.
Q1: How did the mentoring start?
Galina: We know each other from the Omdena project Identifying Economic Incentives for Forest and Landscape Restoration where Susanne did impressive work on the project cloud architecture. I was very pleased when I was asked to be her mentor. We have a lot in common – we both come from Europe, have had long careers and we are both self-taught, hand-on AI enthusiasts with a bad tendency to multitask. She had a couple of Omdena projects under her belt, including one as a Product Owner. After a long stint as an engineer in the events industry, she was actively looking to switch to a career in tech, and her hard work and thorough approach paid off – she started her new job as a Program Manager in a semiconductor automation company in September.
Susanne: When the Omdena career mentoring program was first announced I applied immediately, because I thought it would be helpful to get feedback from someone who is already working in the industry. I have had the chance to do some career coaching sessions with professional career coaches through my Udacity Nanodegree scholarships but unfortunately these coaches did not have any specific knowledge about the Data Science and machine learning job requirements.
Below is the screenshot of the above said project where Galina and Susanne worked on:
Q2. What expectations did you have when you started mentoring?
Susanne: I am a self-taught data scientist and the Omdena projects already provided a good gauge for my technical skills, therefore I wasn’t too concerned about technical skills gaps. I wanted to focus the mentoring on how to present myself as a Machine Learning engineer, because I knew that selling my skills has never been a strength of mine. I had been working in the events industry for more than 20 years and from an outsider’s perspective this kind of career change looks quite strange, while for me it was a logical path. Having Galina as a mentor was a super cool situation, because she knew my work and work style from our common Omdena project. She told me what she considered as my strengths when working on AI projects and getting this feedback was extremely valuable.
Q3. What happened during the mentoring?
Galina: Susanne already knew that she wanted to change careers, she was aware of her strengths. At the same time, she was working on all fronts – she had so many things going on – Omdena projects, a Computer Vision hackathon and a couple of cloud certifications thrown in. We found the common points between her past career and the desired new career, looked up different technology roles and job descriptions, and started working on her LinkedIn profile. We also talked about other options – working with recruiters, self-employment, consulting, even academia. But mostly it was updates since the last call, discussing the job market, exchanging ideas and above all, a friendly chat with a fellow data scientist – Susanne has a wicked sense of humour.
Below is one of the screenshot she followed schedule for mentoring:
Q4: What part of the mentoring process did you find most helpful?
Susanne: The most helpful part was to get independent feedback on my ideas from another data scientist. As a seasoned project manager I had set up a plan with the different areas that I wanted to improve and certifications and courses that I wanted to take. As I was always working on several tasks simultaneously it was extremely helpful to discuss with Galina the different topics and see which areas she would focus on. We were also exchanging ideas and recommendations on suitable learning materials for different topics. For example we were discussing how to prepare for the Microsoft Azure Fundamentals Certification that we now both have passed. Having an accountability partner that constantly asks about the progress on the different topics is an extremely valuable and driving source of external motivation.
Q5: How did the Omdena experience help you in switching careers?
Susanne: Working in the self-organizing Omdena project teams helped me to gauge my technical skills in AI and find out my strength. People often have a blind spot when trying to find their personal strength, because they think that something that feels super natural for them cannot be a strength. I knew that I was good at structuring and organizing projects- something that I have done professionally for more than 20 years, but I did not have any clue that this is a skill that is rare among data scientists and machine learning engineers. So, I redefined my ideal job title as “AI project manager”. Also playing out the combination of the two pillars “machine learning and project management” or “engineering and project management” were the winning combination for several job interviews and the job offer as a program manager in R&D in the semiconductor automation industry.
Q6: What will happen after mentoring?
Galina: After Susanne successfully started her new job, data science career mentoring as such is no longer needed. However, we both felt that we would like to catch up on a regular basis – the personal development journey is ongoing and everybody benefits from having a place to sound out ideas. This will also be the chance to switch roles too – there are so many things I would like to learn from Susanne! This is the best thing about mentoring – it goes beyond job hunting and benefits both parties.
Q7: Do you have any advice for people changing paths mid-career?
Galina: As seen in Susanne’s example: play on your strengths, get a clear vision, and consider all options.
Susanne: A career change is never a straight path and it is vital that you sell your actual skills and not what you would like to do. Find the connecting points between your previous jobs, your newly gained skills and the new position and you will build a convincing story for why you are the right person for that job.
I would like to thank Galina for her time and effort during the career coaching. Living half across the globe it was sometimes difficult to find a suitable time for our coaching calls, but it was always worth it and I was really looking forward to our sessions. We will keep in touch to discuss our ongoing projects and future career opportunities because for both of us it is invaluable to have a sparring partner to bounce back ideas.
Below is the brief intro and bio about our data science mentors and mentees:
Galina is a Product Owner and a Lead Machine Learning Engineer at Omdena. She specializes in applying AI in the areas of Education and Social Enterprise. She has worked as a Statistical Modeller and as a Data Science Manager at the Open University, UK, and, previously, in various roles in the technology sector. She has experience in leading teams and is passionate about mentoring and developing others and about diversity in the technology industry.
Susanne is a Product Owner and a Lead Machine Learning Engineer at Omdena. She has worked as a project manager in the events industry for more than 20 years and has just started her new role as Program Manager in a semiconductor automation company. Susanne loves to solve problems through the use of technology and prefers to work with multicultural and interdisciplinary teams.