Journey into Machine Learning and Data Science Part 1

Written by Salomey Osei.

This was our very first event as a chapter in 2020. It was a panel discussion made up of highly impactful women who gave great insights about their journey in ML and Data Science.

It started with the story of how we came together to create this organization, an introduction of the organizers and then the discussions began. The panel discussion brought together successful women in Machine Learning and Data Science to share their success stories and experiences for other women to get started in the field.

First, one of our co-organizers Deborah introduced the brains behind the organization and the team that made this possible. This idea of creating this organization started with Deborah, Salomey and Aseda. There had been discussions between Aseda and Deborah at Deep Learning Indaba Kenya about creating an all-inclusive and female-represented group to teach both the young and the old about this emerging field "Machine Learning and Data Science".

The coming years brought this idea into light when Debrah met Salomey during their master's degree at the AIMS Ghana AMMI program where they both decided to give back to the community following the encouragement from the then Head of Google Accra, Moustapha Cisse to get involved with the community and pass on their knowledge to others. The organization then successfully launched and this was our first program where we had a panel discussion on the journeys of some amazing women in the ML and Data Science community.

Moderators

The moderators for this event were Salomey and Aseda, all co-organizers of WiMLDS Accra chapter.

Panelists

1. Nyalleng Moorosi (now Senior Researcher at The Distributed AI Research Institute (DAIR), formally Software Engineer at Google AI)

2. Sarah Oppan (now Associate Director at EY Technology Consulting Data and Analytics, formally AVP, BI, Analytics & Big Data at Ecobank)

3. Amerley Ampofo (Customer Insights & Analytics Senior Manager)

4. Sara Hooker (now Head of Cohere For AI, formally Research Scientist at Google)

5. Usha Rengaraju (Chief of Research at Exa Protocol)

This part of the session was led by Aseda. She thanked the panelist and gave a warm introduction to the panelist and allowed them to also talk about what they do and what their research area entails.

Question: Tell us about yourself and take us through your journey into Machine Learning and Data Science.

Amerley: She works with Mtn and her job is to support decision-making by forming a bridge between the IS and commercial where she works with data, tools and technology and follows processes most importantly her work involves working with people across the business. This is underpinned by a data-driven strategy that MTN has. On a day-to-day basis, she works with a larger team from diverse teams to understand these data and technologies to influence decision-making.

At the time of this panel discussion, she was 20 years into the area. After graduating from the University of Ghana, she was sure that she was passionate about Data Science but was not sure of what could come out of it. At the time, there was a new technology that had come up "Geographic Information Systems" which she thought was interesting as this was used to solve problems that evolved then and now.

Sarah: She grew up in Mozambique in southern Africa, she graduated and her goal at the time is so different from what she does now. She read Economics with an interest in how to model the world. This resulted in an undergraduate in economics with a dream of working with the world bank. She went to California for a break and to start her Ph.D. in Economics whiles working Economic consultant and volunteering on the weekend with nonprofits from the communities she grew up in (Africa) on their data.

Through this, she discovered herself in Machine Learning and found it so interesting that one could model real-world data while using linear models. So she explored more advanced model tools and continued to explore. She taught herself to code during the mornings and evenings and joined a start-up just to grow technically.

She said that the component of her journey that was generalizable was that she was very passionate about all she did and the questions she sought to answer. She also mentioned that there was a component of luck in her journey and this comes from having the mental strength to continue until you find your element of luck. Her luck came when her director gave her a big break that helped her transition from being a data analyst into Engineering even though she thought she was not good enough.

She became much stronger from then and started teaching Machine Learning. She later returned to Kenya because her parents had moved and she taught Machine Learning there. She was then offered a job at Google which was an exciting journey for her. She also mentioned that her research goes beyond just test and accuracy, how to train models that are resource efficient that can be deployed to regions where it is limited or not much connectivity. She is also interested in fairness and interpretability, how can we make sure that ML models are inclusive and accessible and empower others to use ML.

Usher: She initially did not have the ambition to start as a data scientist but she was a financial consultant with all her journey in Finance. She took a break to take a CFA exam and while looking for tools to learn, discovered Coursera and took courses for the unlimited offer of 50 USD per month. She took a wide range of courses including courses related to ML and Data Science. One day she got a call that they were building a quantitative end-to-end product and were interested in someone with a financial and ML background. So she joined as a part-time consultant. She continued in this direction and started working as a consultant for other companies.

Nyaleng: she started with her undergrad which had some form of biology in it. She was a computer science student and halfway through the journey, she started thinking about where her interest was. She decided to do bioinformatics toward the end of her study ad that is how it started. After that, she discovered that she was interested in discovering patterns in data. She did a liberal art degree which was different from engineering. Her first job was as a software engineer with little coding skills. She worked through it and later started a graduate degree (Ph.D.).

Halfway through she got distracted and started to research Economics, she left the program again after so much confusion and left for Africa to teach. Then she discovered that she was not teaching the way she wanted and that she was more of a one-on-one person. She later quit teaching to work for a government organization as a data scientist. She applied and got a job joining Google Ghana.

She stated that she has worked hand in hand with Sarah and her goal was also much similar to that of Sarah. She has researched Privacy and Fairness for ML. She believes that ML is a tool that can work for everybody but not be used to endanger anyone. She continued to be one of the founding members of the Deep Learning Indaba which has since grown. Indaba was created so that Africans can do and implement these technologies themselves and by the way they understand their world.

Sarah Opan: She leads the data analyst group in Ecobank and her mission was to be a software engineer. She found a job that wanted her to be a business analyst and she took the opportunity. This work helped shape her for the future and self-studying also helped her improve in this area.

An opportunity presented itself and since she was curious about how data worked, she took the role of Business intelligence officer. She did self-learning on ML and worked on related projects for Ecobank. She formed a team as the work progressed ad has since led this team. She has also extended her knowledge by mentoring others.

End of part 1

You can watch the full video here.

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