The Power of Mentorship in Data Careers

Marie May • May 19, 2025

The Power of Mentorship in Data Careers


Mentorship is one of the most powerful tools in shaping successful careers, especially in the fast-evolving world of data. For early-career professionals navigating complex tools, technical skills, and industry expectations, a mentor can offer invaluable guidance, encouragement, and direction. 


A great mentor can help bridge the gap between academic knowledge and practical application. They provide clarity on career goals, help refine your data storytelling abilities and offer insight into the dynamics of data teams and industry trends. Whether you're learning how to craft a compelling portfolio or make sense of your first data project at work, a mentor’s perspective can be the difference between confusion and confidence.

 

Finding the Right Mentor in the Data Industry 

So how do you find a mentor that aligns with your career goals? Start by identifying individuals who are doing what you aspire to do—whether they’re analysts, data scientists, data engineers, or AI strategists. LinkedIn, professional forums, data communities like DataTalks. Club, and meetups (virtual or in-person) are all excellent places to begin your search. 

When reaching out, be respectful of their time. A concise message explaining who you are, why you admire their work, and what kind of guidance you’re seeking can go a long way. You might say: "I’ve been following your work on responsible AI and found your recent talk really inspiring. I'm an early-career data analyst eager to improve my model evaluation skills. Would you be open to a brief chat or mentorship?" 


Once the relationship is established, make it a two-way street. Be prepared with thoughtful questions, share your progress, and offer your own insights where relevant. Mentorship works best when it feels collaborative rather than one-sided. 

https://www.forbes.com/councils/forbestechcouncil/2023/12/06/why-mentorship-is-crucial-for-data-science-and-ai-professionals/?utm 

 

Becoming a Mentor and Building a Culture of Knowledge-Sharing 

As you progress in your own career, there comes a time when you can and should give back. Becoming a mentor isn’t just about sharing knowledge; it’s about uplifting others and strengthening the data community. 

You don’t need to be a senior executive to offer mentorship. If you’ve been in the industry for a couple of years, there’s already someone a few steps behind you who could benefit from your experience. Sharing your journey through blog posts, webinars, or one-on-one sessions creates ripples of learning that extend far beyond one individual. 

Mentorship also benefits the mentor. Teaching reinforces your own understanding and encourages empathy, communication, and leadership skills qualities that are invaluable for long-term career success. 



Ultimately, mentorship is a cycle. You seek guidance, grow from it, and then pass it on. That’s how we build not just careers, but a thriving, supportive culture in data and AI. 

Contact Marie May At Eden Smith to chat more about our Nurture Programme and how it could help you. 


By Christa Swain December 3, 2025
Executive Summary: AI, Ethics, and Human-Centred Design Our recent Leaders Advisory Board event - designed in partnership with Corndel - featured three engaging sessions that explored how AI impacts human cognition, customer experience, and fairness. Here's what we learnt: 1. Think or Sink – Are We Using AI to Enhance or Reduce Cognitive Ability? Speaker: Rosanne Werner , CEO at XcelerateIQ & ex Transformation Lead at Coca-Cola Roseanne opened the day with an interactive and thought-provoking session, firmly positioning AI: “AI should be your sparring partner, not your substitute for thinking.” Her research revealed a striking insight: 83% of people using LLMs couldn’t recall what they wrote, compared to just 11% using traditional search . The message? It’s not about avoiding AI, but using it in ways that strengthen thinking , not outsource it. Roseanne explained how our brains form engrams - memory footprints that enable creativity and critical thinking. Over-reliance on AI risks weakening these pathways, reducing retention and problem-solving ability. She introduced the Mind Over Machine Toolkit , six strategies to use AI as a thinking partner: Provide Context First – Frame the problem before asking AI. Use AI as a Challenger – Stress-test ideas and uncover blind spots. Iterative Co-Creation – Collaborate, refine, and evaluate. Document Your Thinking – Keep reasoning visible. Reflective Prompts – Support reflection, not replace judgment. Sparring Partner – Test assumptions and explore risks. Roseanne summed it up with a simple rule: use Sink for low-value, repetitive tasks, and Think for strategic, creative decisions. 2. Designing Chatbots with Human-Centred AI Speaker: Sarah Schlobohm , Fractional Chief AI Officer Sarah brought a practical perspective, drawing on experience implementing AI across sectors - from banking and cybersecurity to rail innovation. She began with a relatable question: “Who’s been frustrated by a chatbot recently?” Almost every hand went up. Through a real-world example (redacted out of politeness), Sarah illustrated how chatbots can fail when designed with the wrong priorities. The chatbot optimised for deflection and containment , but lacked escape routes , sentiment detection, and escalation paths - turning a simple purchase into a multi-day ordeal. “Don’t measure success by how well the chatbot performs for the bot—measure it by how well it performs for the human.” Sarah introduced principles for better chatbot design: Human-Centred Design – Focus on user needs and emotional impact. Systems Thinking – Consider the entire process, not just chatbot metrics. Escalation Triggers – Negative sentiment, repeated failures, high-value intents. Context Awareness – Detect when a task moves from routine to complex and route accordingly. The takeaway? Automation should remove friction from the whole system - not push it onto the customer. 3. Responsible AI and Bias in Large Language Models Speaker: Sarah Wyer , Professional Development Expert in AI Ethics at Corndel “When we create AI, we embed our values within it.” She shared her journey tackling gender bias in large language models , from GPT-2 through to GPT-5, and highlighted why responsible AI matters. AI systems reflect human choices - what data we use, how we define success, and who decides what is fair. Real-world examples brought this to life: facial recognition systems failing to recognise darker skin tones, credit decisions disadvantaging women, and risk assessment tools perpetuating racial bias. Even today, LinkedIn engagement patterns show gender bias! Sarah made the point that simple actions - like testing prompts such as “Women can…” or “Men can…” - can reveal hidden disparities and spark vital conversations. To address these issues, Sarah introduced the D.R.I.F.T framework , a practical guide for organisations: D – Diversity : Build diverse teams to challenge bias. R – Representative Data : Ensure datasets reflect all user groups. I – Independent/Internal Audit : Test outputs regularly. F – Freedom : Create a culture where employees can challenge AI decisions. T – Transparency : Share processes without exposing proprietary code. Wrapping up the final session - before we opened the floor to panel questions and debate - Sarah created the opportunity to discuss how we address AI bias within our organisations by stepping through the DRIFT framework. Shared Themes Across All Sessions AI is powerful, but context matters . Human oversight and ethical design are critical . Use AI to augment thinking , not replace it. Measure success by human outcomes , not just automation metrics. We've had such great feedback from this event series - especially around the quality of speakers and the opportunity to have meaningful conversation and debate outside of functions. Definitely more in the events plan for 2026! If you'd like to be part of the conversation please navigate to our LAB events page to register your interest .
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