Product Management

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Built alpha integration with an EDR (Endpoint Detection and Response) product to assist with a high priority sales process.

I had to quickly learn about the product to write requirements for the integration. Also programmed, tested and deployed code alongside with other infrastructure to fully support the integration.

I took a step back from marketing to work on onboarding for a week. The reason was that I was actually getting a good amount of signups, but most were not sticking around for more than a couple of days.

The classic problem of PKM tools is that they are either a blank slate or filled with content too generic to be useful. That makes onboarding hard.

I attempted to mitigate this with prebuilt notebooks around specific topics. People seem to use them, but they are not having the intended effect of being helpful.

Spent a bit of time revising the software development notebook as that was the most commonly used one.

It used to be just a collection of random notes I had, which now seems obvious are not useful to many people.

Now I have sections explaining the random notes and how people can use that as inspiration for their own note taking.

I also have new sections for notes from when I was learning new technology so people can use that as inspiration as well.

I suspect some people may be adding this notebook wanting to learn to code which is not what it provides. I am looking into adding that in the future.

Here is a screenshot of the new notes.

As part of NLP class during Master's program, here is Product Requirement Document (PRD) I wrote for our group project. Besides this PRD, I built MVP web app using Flask + JS + SoundCloud API.

Title:
Sentimental Music Box

Overview:
Our app recommends songs to students based on sentiment and subjectivity of lyrics. Inspired by why and how recommendation engines bring value to users and how Spotify nailed it with Discover Weekly and other custom playlists, we decided to build our own music streaming service for a segment of users we knew a lot at that time: students.

While the majority of recommendation systems in production are based on users' collective usage (Collaborative Filtering), we know that the state-of-art systems, deployed by Spotify as an example, take into account the content (lyrics/rhythm). We hypothesized that for students, who prepare for exams, the lyrics itself will bring the most value.

Our idea relies on unveiling and categorizing songs based on the emotions and subjectivity that are transmitted by the lyrics of the song and not by its rhythm. Check Alors on danse by Stromae, which people loved to dance to:

Et là tu te dis que c'est fini car pire que ça ce serait la mort
Quand tu crois enfin que tu t'en sors, quand y en a plus et ben y en a encore

And then you tell yourself it's over because worse than that it would be death 
When you finally believe that you get out of it, when there is more and well there is still

Objectives

• Test the hypothesis that content-based recommendations are valuable in certain use cases
• Test the application of current libraries, used in sentiment analysis (text analytics)

Success metrics

• [Potential] Usage, measured by the feedback of the class ("Raise your hand if you'd use it?")
• Accuracy of sentiment analysis, measured by the feedback from the class

Messaging
Prepare your mind for flow state during studying by listening to appropriate music.

Persona(s)
A university student

User scenarios
A student named Alen is planning to study for an exam on Machine Learning. It is really important one, but there are so many things going on in his head. Moreover, he is not really prepared, but the exam is scheduled for next week and he starts to feel a little bit desperate. Alen decides to listen to music to block outside noise and better concentrate, he opens the music app, chooses the subject he is planning to study now, the level of readiness, and general genre he prefers. The app shows him the list of songs. Alen now can add songs to his playlist and start listening and prepare himself for the upcoming exam.

User Stories/Features/Requirements

• As a student, I want to add songs to my playlist so that I can listen to songs that I chose without interruption.
• As a student, I want to choose subject I am preparing for so that I can listen to music that is more relevant for this subject.
• As a student, I want to choose genre of music so that I can listen to favourite genre.
• As a student, I want to choose how well I am prepared so that I can listen to songs with lyrics whose meaning will be more relevant.

Q&A

• We used TextBlob for sentiment analysis. It gives two values for a text: Polarity and Subjectivity. Polarity value ranges from -1 (negative sentiment) to +1 (positive sentiment). Subjectivity value ranges from 0 (very objective) to 1 (very subjective).
• We hypothesized that when a student is studying hard science (i.e. Math or Python), her mind needs to adapt to objectivity and therefore our app suggests songs whose lyrics are estimated to be objective (Subjectivity is closer to 0). On the contrary, when she studies for soft science (i.e. Ethics or Literature), we think that songs with very subjective lyrics will help in creativity and expressing her own opinion.
• We hypothesized that when a student is desperate (because not prepared enough given the time until the next exam), her mind needs songs with positive words, therefore our app suggests songs with Polarity closer to 1. On the contrary, if the student thinks she is ready, our app suggests songs with more negative polarity to balance out the excessive optimism.
• For the MVP web app, we used MetroLyrics database of lyrics, downloaded from Kaggle.
• We tailored the UI for our Master in Business Analytics & Big Data program at IE.

Design
Below there is UI of the web app I built (not perfect, but does the job with my at the time basic knowledge of JavaScript).

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Excited to have started this 8-week course, with a lot of hands-on work to get experience to transition to working in Product.  This experience will allow me to apply my consulting skill-set in a more versatile role! Looking to do a lot of introspection work as well to figure out what domain to do a deep dive on.