Data science freelancing is a test of both technical skill and strategic adaptability.

In our conversation with Jérémy Arancio, we dissect the frameworks he uses to thrive: relentless upskilling, leveraging platforms like LinkedIn for visibility, and distinguishing between hype and practical tools in AI.

From the nuanced differences of GenAI vs. NLP to the tactical mindset shift freelancing requires, this episode breaks down what separates sustainable data science careers from those that fizzle out.

Top Insights:

• Freelancing is a means to learn and grow.

• You will never feel fully prepared to start freelancing.

• The journey of freelancing can be both rewarding and challenging.

• AI should be used to solve specific business problems.

• Content creation can help build connections and opportunities.

• Choosing the right vehicle for your career is crucial.

• Growth often comes from discomfort and challenges.

• Networking on platforms like LinkedIn is valuable for career advancement.

• Understanding the difference between Gen.AI and NLP is important.

• Continuous learning is essential in the fast-paced tech industry.

Connect with Jeremy Arancio

• Jeremy Arancio on LinkedIn - https://cz.linkedin.com/in/jeremy-arancio

• Read his articles - https://medium.com/@jeremyarancio

• Jeremy Arancio on GitHub - https://github.com/JeremyArancio

• Email him - jeremyarancio.freelance@gmail.com

  
  

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/

• Let’s Talk AI - https://thomasbustos.substack.com/

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted by Ausha. See ausha.co/privacy-policy for more information.

AI is evolving fast, but are our systems, ethics, and infrastructure keeping up?

In this episode, Thomas Bustos and Marijn Markus break down the complex interplay between innovation and responsibility in artificial intelligence and machine learning. They examine current limitations in explainability and bias, and unpack what it will take to build scalable, transparent, and human-centric systems.

With examples from healthcare and finance, and a strong focus on aligning AI with social good, this episode offers both technical depth and strategic perspective. Whether you’re designing ML pipelines or debating LLM regulation, this conversation delivers insight you won’t want to miss.

Top Insights:

• Ethical and Data Challenges: AI systems must navigate ethical considerations and data privacy concerns, ensuring transparency and accountability.

• Innovation and Impact: AI has the potential to revolutionize industries, from healthcare to finance, by driving advancements and creating new opportunities.

• Regulatory and Legacy Issues: Balancing innovation with necessary regulations and overcoming legacy systems are key challenges for AI adoption.

• Bias and Misinformation: Addressing bias in AI models and combating misinformation are critical for ensuring AI's positive impact on society.

• Job Market Evolution: While AI may automate some jobs, it also creates new opportunities, emphasizing the need for adaptability and skill diversification.

• Human Responsibility: The importance of human oversight in AI systems is crucial, as technology should serve humanity's best interests.

Connect with Marijn Markus

• Marijn Markus on LinkedIn - https://www.linkedin.com/in/marijnmarkus

• Capgemini - https://www.capgemini.com/

  
  

Connect with Thomas Bustos & Let's Talk AI

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/

• Substack - https://thomasbustos.substack.com/

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted by Ausha. See ausha.co/privacy-policy for more information.

What do you really need to thrive as a data engineer today?

Ananth Packkildurai joins us to cut through the noise. From his experience building systems at Slack to his current role in data engineering, Ananth reveals what skills truly stand the test of time, like SQL, data modeling, and a deep understanding of user needs.

We also explore how the rise of Generative AI is changing the game, what observability means in practice, and why chasing the latest trend might not be your best move.

Clear, practical, and refreshingly honest.

This episode is for anyone who wants to grow with intention.

Top Insights:

• Data engineering is at a pivotal moment, similar to the industrial revolution.

• The demand for data engineering skills is rapidly increasing.

• Understanding SQL is crucial as it constitutes the majority of data workloads.

• Feature prioritization should focus on high yield, low effort projects.

• Observability is essential for building reliable data systems.

• Scaling challenges often arise from unexpected user demand spikes.

• Product thinking is important in data engineering to meet user needs.

• GenAI has the potential to revolutionize data engineering practices.

• Exploring various aspects of data engineering is beneficial before specializing.

• Real-world observation can enhance understanding of data engineering concepts.

Connect with Ananth Packkildurai

• Ananth Packkildurai on LinkedIn - https://www.linkedin.com/in/ananthdurai

• Data Engineering Weekly by Ananth Packkildurai - https://www.dataengineeringweekly.com/

  
  

Connect with Thomas Bustos

• Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/

• Let’s Talk AI - https://thomasbustos.substack.com/

• Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai

• Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted by Ausha. See ausha.co/privacy-policy for more information.