The Effective Data Scientist

Sources

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ENBIS – Recipients of the George Box Medal

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ENBIS-26 Box Medal session timetable

A conversation with Glen Wright Colopy on configuring useful AI tools, checking their work, and knowing when to do the task yourself.

In this episode of The Effective Data Scientist Podcast, we sit down with Jeff Stevenson, CMC MSAT Specialist at Boehringer Ingelheim and one of the most influential “Data Evangelists” in modern biopharma. Jeff shares how 30 years across Quality, Supply Chain, and MSAT shaped his philosophy on integrity, responsibility, and the true meaning of data readiness.
Together, we explore the STIPS mindset, why most companies still fall into the IT Trap, how psychological safety drives technical excellence, and which AI tools are finally helping teams achieve analytic‑ready data. Jeff closes with a powerful reflection on what he would do differently if he were starting his career today.

Episode Summary

How can you get better, more reliable results from AI?

In this episode, Paolo and Aziza explore why effective prompting is about much more than asking good questions. We discuss how structured prompting frameworks such as ROSES, CRISP, CARE, TRACE, and APE can help data scientists provide clearer instructions, reduce ambiguity, and improve the quality of AI-generated outputs. We also share practical examples of applying these frameworks to real-world data challenges and discuss why designing how AI should think is often more important than the answer itself.

Episode Highlights

• Why prompting is closer to writing an analytical brief than asking a question
• An overview of popular prompting frameworks including ROSES, CRISP, CARE, TRACE, and APE
• How structured prompts improve reliability and reduce ambiguity
• Practical considerations when using AI for data transformation and analysis-ready datasets
• Why data scientists should focus on defining assumptions, constraints, and expected outputs
• How AI can help refine and improve prompts over time

Key Takeaway

Don't just ask AI for answers. Design how it should think. Clear structure, explicit assumptions, and well-defined expectations lead to better outcomes and greater trust in AI-generated results.

Leadership is often misunderstood as something reserved for managers and executives. In this episode of The Effective Data Scientist Podcast, Aziza Yormirzaeva, Paolo Eusebi, and Alexander Schacht explore why leadership is a critical skill for every data scientist, statistician, and quantitative professional—regardless of job title.

Drawing from personal experiences, mentorship, leadership training, and real-world examples, the conversation highlights how data scientists can increase their impact by developing ownership, listening skills, trust, communication, and influence. The hosts discuss why technical expertise alone is not enough to drive successful projects and how leadership principles can help professionals turn ideas into action.

They also explore the importance of understanding business problems, building trust across teams, embracing accountability, and developing the human skills necessary to succeed in increasingly cross-functional environments.

Whether you are early in your career or already leading teams, this episode offers practical insights on becoming more effective, influential, and impactful in your work.

**Why You Should Listen ** Many data scientists spend years developing technical expertise but receive little guidance on how to influence decisions, collaborate effectively, and create lasting impact.

**In this episode, you'll learn: **

• Why leadership is everyone's responsibility—not just managers'
• How an ownership mindset changes project outcomes
• The role of trust, listening, and communication in successful teams
• Why understanding business needs matters as much as technical accuracy
• How to develop leadership skills throughout your career
• Practical ways to become more influential and effective in cross-functional environments

**Episode Highlights **

• [00:00] Introduction to leadership in data science and why the human side of technical work matters
• [03:15] Leadership is not limited to managers—everyone can lead
• [06:20] Owner mindset vs. renter mindset: taking responsibility for project success
• [10:05] Why leadership skills require deliberate learning and practice
• [13:00] Resources for developing leadership capabilities, including mentorship, books, and training programs
• [18:30] The transition from academia to industry and the importance of learning how organizations operate
• [22:15] Listening as the most important leadership skill
• [26:10] Building trust through care, character, and competence
• [31:00] Why care and character often matter more than technical expertise
• [35:15] Understanding the business problem behind every analysis
• [40:00] A real-world example of how curiosity and leadership created significant business impact
• [44:30] Team dynamics and lessons from The Five Dysfunctions of a Team
• [49:20] Trust, accountability, healthy conflict, and teamwork
• [53:45] Final leadership advice for aspiring data scientists and statisticians

Episode Summary

I’m excited to welcome you to the relaunch of The Effective Data Scientist Podcast.

In this episode, I’m joined by my new co-hosts, Aziza Yormirzaeva and Paolo Eusebi, as we introduce what this podcast is all about and why we decided to bring it back.

Together, we explore what it really means to be an effective data scientist today. It’s not just about technical expertise—it’s about influencing decisions, collaborating with others, and making sure your work actually creates impact.

We also share our vision for the podcast: helping you grow across all areas of your career, from technical skills to communication, leadership, and navigating change in a fast-evolving field.

Why You Should Listen

• You want to become more than just a technical expert—I’ll help you become more effective in your role
• You’re navigating change in data science, including AI and new technologies
• You want to improve how you influence decisions and work with stakeholders
• You’re looking for continuous learning and practical career growth
• You want to hear real conversations with experienced professionals across industries

Episode Highlights

00:00 – Welcome & Relaunch Why I’m bringing back the podcast and what’s ahead

01:12 – Meet Aziza & Paolo My co-hosts introduce themselves and their journeys

03:29 – Where Data Science Is Heading How statistics, machine learning, and AI come together

04:52 – What Makes a Data Scientist Effective Why technical skills alone are not enough

06:19 – Turning Analysis into Impact How to influence decisions and drive change

07:41 – Staying Up to Date Why continuous learning is essential today

09:33 – Supporting Every Career Stage From beginners to experienced professionals

09:56 – Building a Community Our vision for connecting data scientists globally

12:09 – What You Can Expect Next Topics, guests, and future plans for the show

Discussion with Alexander Schacht and Benjamin Piske

• how it relates to your goals,
• what it takes to think strategically,
• which role innovation has here,
• what practical steps to take to drive teams forward,
• which knowledge to acquire to lead teams successfully,
• how this relates to influencing,
• and how your attitude will play a big role in this.

Interview with Shafi Chowdhury
In this episode, we’ll cover an amazing story by one of the best programmers and mentors I ever worked with - Shafi Chowdhury (www.shaficonsultancy.com).

We’ll explore how it changed from being a freelance programmer only to building his company on the side. He had a great vision in mind, that drove him forward.

You’ll also hear about his approach to teaching and mentoring – or in general helping people do their job better. His abundance mindset inspires me a lot. Shafi explains, why and how he made his own job redundant in his own company.

Retention is a major cost driver and disrupts some companies a lot. You’ll learn how he manages to have nearly no turnover in his company and how is approach to recruiting and training new employees fits in to this.

Further, we’ll cover how and why Shafi and his team regularly presenting at different conferences. We’ll especially go deeper on this presentation at the PSI conference in 2017 about an amazing tool to analyse and visualize data at the same time. His approach to delivering all this innovation is very unique.

Finally, you’ll learn a lot about the leadership attitude that helped him grow his business fast into a medium-sized CRO with a very stable client base.

Discussion with Alexander Schacht and Paolo Eusebi
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Discussion with Alexander Schacht and Paolo Eusebi
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Do you have a lot of email ping-pong, where emails go back and forth many times – too many times?

Are you aware about the brand of you, that you communicate with your email style?

Is email your default communication tool?

Then this episode is for you. We have researched various articles on good email writing copies and distilled the best for you in this episode.

By listening to this episode, you will learn about:

When is email the best way to communicate?
Approaches to attachments.
How to take care of your style?
How to structure your emails?
About different email cultures
Simple actionable tricks to improve your effectiveness directly

Discussion with Alexander Schacht and Benjamin Piske

By listening to this episode, you’ll learn about these topics:

• What are helpful mindsets about emails
• Five step approach to managing emails
• Good habits to establish like
• Reply in a timely manner
• Send and respond less to receive less.
• Tips on how to set up filters
• Smartphone vs desktop email checking
• When and when not to work on emails
• And the option of last resort

Discussion with Alexander Schacht and Paolo Eusebi
In this episode, we share out ideas and experiences, which mindset sets up statisticians for success. We cover topics around:

Discussion with Alexander Schacht and Benjamin Piske

In this episode, we share our ideas and experiences, which mindset sets up for success. We cover topics around:

Leading people

Convincing business partners

Delivering value and selling it–and what does selling mean

Thinking outside the status quo to improve things in the long run

Always learning about the business and the people in the business

Learning about statistical innovation

Doing things more effectively

Becoming more impact-fully

Raising your business acumen–internally and externally

Having quality in mind

Quote from the Episode by Bill Gates “Most people overestimate what they can do in one year and underestimate what they can do in ten years.”

Discussion with Paolo and Thomas
Communicating data is so important! Quarto is a fantastic tool for writing reproducible reports
using literate programming. Literate programming allows us to incorporate documentation and
code in the same program. The data science community has embraced this idea by adopting
Rmarkdown and Jupyter Notebooks. Using Quarto efficiently, you can create parametrized
reports, write scientific publications, and build data-driven slides.

Enjoy this super-interesting conversation with Thomas Neitmann, and be an effective data
scientist!

Resources:

R Packages (2e)

In this episode, Paolo and Thomas dive into the fundamental principles for a well-structured data science project. These include practical advice on:

• organizing files into folders,

• documenting and commenting code,

• using version control systems and much more.

Although the episode focuses on applying these fundamental principles in R projects, you want to apply the same principles to any Data Science project, regardless of the language used.

Further resources:

Advanced R 2nd Ed. (http://adv-r.hadley.nz)

R for Data Science 2nd Ed. (http://(r4ds.hadley.nz)