I'm now part of a data and AI team in a fintech spinoff. When I joined the company, it did not make sense to spend time in defining precise job titles because we were to build everything from scratch (both software, teams and organization). My job title was therefore a generic "AI Practitioner". One year later, teams and responsibilities are more clear, and it is now time to define my job title.
What was I doing up to now?
I have a background in data science and software engineering. I started my career in 2013 as "Data Scientist and Software Developer" (what we would call today a Machine Learning Engineer?) in a small startup. I was then defined as an "Associate" when working as a data scientist in a consulting firm. In the last 3 years, I worked in a manufacturing firm as "Data Scientist".
What am I doing now?
In the company I currently work at, I work in the data and AI team. My main activities include:
- planning and prioritizing of our data solution
- designing our data and software architecture
- developing in first person our data integrations, analytics reporting, ML models and data solutions
- making sure our Scrum cerimonies run smoothly
My job has a mix of coding, architecture design, and project/product management. Why such a variety of responsibilities? I work in a small team part of company that is growing quickly starting from zero. Each team is quite autonomous in doing their work by taking an end-to-end ownership of the activity. For example, in my data and AI team we handle our work end-to-end. We are responsible for the entire pipeline: definining roadmaps, development, deployment, and monitoring.
My job title?
It is now time to define a job title that can summarize my responsibilities listed above. These are some alternatives I took into consideration:
| Job title | Comments |
|---|---|
| Senior Data Scientist/Engineer | Too vertical on a piece of the pipeline compared to the spectrum of activities I work on |
| Data Architect | Nicely defines the technical activities of designing and scaling our data solutions, but lacks the ownership of the backlog and of the product roadmap |
| Data Product Owner | States clearly the ownership of the product backlog, but I feel that the "Product Owner" title is too tight to a Scrum role and lacks of technical responsibilities |
| Lead Data and AI | States the responsibility of leading a team of experts in a domain. However, it does not feature any ownership on the product roadmap. Furthermore, it states a clear hierarchy in the team that goes against our team and company culture (a culture of distributed ownership and flat organization) |
I was not satisified with the job titles above. Then, I came up with "Data Product Manager". I felt this job title was what I was looking for because:
- as a Product Manager, you are responsible for the product roadmap and strategy
- the prefix "Data" adds a technical taste. By doing some research, I found that a TPM (Technical Product Manager) is a common job title that defines a product manager that is also in charge of the technical side of the product (architecture, etc)
- it states the ownership of our data product but does add any hierarchy-sounding adjectives
- my end-to-end range of activities can fit well in this definition
I shared these thoughts with my manager that agreed both on the definition of my responsibilities and on the job title. Let's see if these notes can help those that are facing the same challenge of choosing their own job title.