Lessons from a Broad & Varied Data Science Career | Arcenis Rojas | Data Science Hangout
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We were recently joined by Arcenis Rojas, Data Scientist at Indeed, to chat about econometrics, public vs private sector data science, navigating a varied career trajectory, AI integration in the hiring sphere, and making friends at conferences.
In this Hangout, Arcenis talked about how his career journey has been wide as opposed to vertically narrow. He shared that this breadth of experience has given him confidence that he can quickly figure out any dataset. He feels it also taught him how to communicate effectively about data to people at different levels and across various domains. He also shared his tech stack at Indeed, including RStudio, Positron, AWS, Snowflake, Quarto for reporting, Shiny for apps, and Posit Connect for deploying them.
An attendee asked about the impacts of AI on the job search space, and Arcenis shared the AI at Work Report (linked below) from the Indeed Hiring Lab. He says, based on research, generative AI is expected to assist many people but only replace small segments of the workforce in the coming 5-10 years, and that entry-level knowledge work is predicted to be the most highly impacted area.
Resources mentioned in the video and zoom chat:
Indeed Hiring Lab: AI at Work Report 2025 → https://www.hiringlab.org/2025/09/23/ai-at-work-report-2025-how-genai-is-rewiring-the-dna-of-jobs/
To Explain or to Predict? (Galit Shmueli, 2010) → https://arxiv.org/abs/1101.0891
Announcing the 2025 table and plotnine contests → https://posit.co/blog/announcing-the-2025-table-and-plotnine-contests/
If you didn’t join live, one great discussion you missed from the zoom chat was about the wide variety of data types data scientists work with. Attendees shared that their data included genomics, finance/trading, environmental/natural resources, e-commerce products, and medical/clinical data. What kind of data types do you work with?
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Timestamps
00:00 Introduction
06:16 "What do you like to do for fun?"
08:51 "What are the unique aspects of financial and economic data science?"
15:07 "What are econometrics?"
16:02 "Is the difference that hard sciences stats is trying to explain what happened where econometrics might be what might happen in the future?"
19:39 "Suggestions for making data friends and going to a conference alone."
23:26 "Do you see any misconceptions about the job market online, specifically the ATS thing?"
29:52 "How has your varied career trajectory been an advantage or a challenge in data science?"
34:08 "How is the recent hype wave of AI integration manifesting in the hiring sphere?"
40:08 "What are the tools that you use in your job for reporting?"
41:42 "How do you know when it is time to pivot and leave your role because your skills are stagnating?"
45:56 "How would you persuade leadership to use R or Python?"
49:32 "Did you find yourself always trying to use more complex models when simpler ones would serve the audience better?"
plotnine
positron
Quarto
rstudio
Shiny
