Spotlight: Kolabtree’s Data Science Expert Dr. Tomoe G

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Dr. Tomoe is a scientific consultant with a versatile background in data science, biotechnology, and industrial engineering. With a background that spans both industrial and academic settings, Dr. Tomoe has worked as a Quality Manager at a biotech startup, a researcher, and more recently, as a scientific consultant. Here she discusses her journey, highlighting her skills in quality assurance, machine learning, and transfer learning, as well as the importance of adapting to the fast-evolving fields of data science and consulting. 

Overview of your career and expertise: 

I became a freelance data scientist over a decade ago because of the opportunity to work on diverse, challenging projects that would allow me to leverage my interdisciplinary skills. 

My journey started as a programmer, and pharmacist (already a multidisciplinary field), and moved to biotech and industrial engineering. 

You could say I’m restless. I always loved new challenges.  

Experience in implementing quality assurance management systems, developing product quality specifications, and strong statistical background. Advanced pharma and biotech skill set, not used in the clinic (clinical pharmacy, pharmaceutical assistance). My varied skill set was already difficult for conventional workplaces to comprehend even before I pursued my Doctorship in Industrial Engineering, but relevant in the context of a startup company where I encountered myself. I worked with spin-off companies and tech transfer. I spent some time working as a university professor and a practical academic researcher (post-doc), even securing a public research grant for a technology transfer project.  

I can’t deny I love that freelancing offers me a desirable work-life balance but still with challenges and opportunities to grow my skills continuously; I appreciate the flexibility to set my own hours, be present for my child, and avoid the stresses of night teaching or suddenly missing babysitter problems. 

I became a scientific consultant as I wanted to explore opportunities beyond publishing and metrics, focusing on practical needs and greater autonomy. As a scientific consultant in Kolabtree, I can use my science and research skills on interesting and challenging projects, irrespective of any (local) national economic or political climate that may limit funding for innovation and R&D.  

Being a practice-oriented scientific consultant, I can use a broader view. I can assess the problem and determine the most suitable approach, tools, and so on, without being constrained by preconceived notions about what will garner more citations or publications or define the lab/research group.  

Being a scientific consultant, or working with data science and machine learning, also demands continuous learning, researching and updating of my skills, which perfectly suits my restless nature. 

Interesting project or research you’ve worked on: 

Data science is not just about knowing stats, how machine learning works, and how to train, transfer, or fine-tune models. Is also about understanding the nature of the data and the problem, its context.  

So we need to answer several sometimes apparently unrelated questions like: 

  • How to describe how the market, companies and people change, make decisions, or react, to set the environment and objective function for a reinforcement problem?  
  •  How to look at wave data, look at it as a time series, and convert it into an image? Bring the notion about waveforms, frequency decomposition, resampling and how they influence each other, etc.  
  • How do certain medical conditions influence the outcomes and how to use such information to set a good balance in the sample set, and set a quasi-experiment to describe it properly? 

More recently, I have been working on some projects transferring large models, especially vision models, for interesting applications. Among my projects is an interesting collaboration via Kolabtree, focusing on physiological data, a subject that I am passionate about. In that project, it has been so exciting to see how even my weakest skills can be useful in solving complex tasks. A physics background wasn’t a significant part of the industrial pharmacy curriculum at my university. To my surprise, my background in physics, electromagnetism, and waveforms proved surprisingly useful in reshaping the data and machine learning model transfer learning strategy. I understand now I gained these skills while working with advanced equipment as a quality assurance manager at a biotech company, without even realising it. 

Key global trends in your field or the freelancing industry: 

Transfer learning has become standard practice in several fields, thanks to transformer models. It simplifies the modelling process. The requirement for massive datasets and the evaluation of multiple deep learning model architectures can be obviated through the resourceful repurposing of pre-trained models, even in seemingly unrelated domains. 

While some understanding of model architectures remains necessary, this simplifies model preparation, requiring less data and freeing up creativity and domain expertise to develop novel applications, shifting the focus to domain transfer. 

I want to keep leveraging my diverse skill set and domain expertise to contribute to these different areas. 

Read more about Dr Tomoe G or contact her directly for a project here

This piece is part of Kolabtree’s Spotlight feature, where we showcase the lesser-known aspects of our extensive network of freelance scientific consultants, writers and other PHD-qualified experts who are available for on-demand hire

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