Now I’m in a very different place financially. I feel both grateful and proud of how far I’ve come, and humbled when I think about that version of me—the one who just kept going and trusted it would work out. It also reminds me that every stage of life comes with its own worries and challenges, even if they look different over time.

I’m still that same girl: independent, full of faith, believing the world is yours if you’re willing to work for it!

I’ve seen brilliant, capable people stuck in the same role for years — while others with similar skills and experience rise faster. The difference is rarely talent, and almost never effort alone. Promotion is a complex equation, shaped by human and organizational factors. It requires alignment between your performance, your manager’s advocacy, and the company’s needs. Miss one of these, and it won’t happen — no matter how hard you work.

Here’s a mantra I live by: “You don’t ask, you don’t get.” Confidence in career conversations grows with experience, but even the humble can ask effectively if they back it up with data, examples, and measurable impact. I remember how uncomfortable and shy I was having these conversations many years ago, so I feel you.

This post is inspired by The Manager’s Path by Camille Fournier, my own experiences, and the experiences of friends and colleagues across multiple organizations. It’s not a promise. Not a guarantee. Just an honest, practical collection of thoughts on how to maximize your chances of promotion — without burning yourself out or blaming yourself for things you can’t control.

The Reality: Promotion Requires Three Things

To get promoted, three conditions must be true at the same time:

1. You are performing at the next level.

2. Your manager supports and sponsors you.

3. There is an organizational need or opportunity for that role.

Miss any one? Promotion is unlikely.

I’ve seen high performers who weren’t promoted because they were too passive, strong candidates blocked by timing or politics, and people doing “everything right” in teams that simply had no room to grow. The hard truth is that you can only fully control your own performance and behavior; everything else is partially outside your control.

You Are in the Driver’s Seat

The people I’ve seen progress the most — both in my career and through friends’ experiences — were intentional. They made it clear where they wanted to go, explained why that path made sense, and took deliberate steps to get there. Being vocal about aspirations isn’t pushy. No good manager will be offended; in fact, it gives them something concrete to support.

Manager 1:1s are too often treated like only status updates. That’s a mistake. Use them to discuss your aspirations, the skills you want to grow, feedback on gaps or perception, and potential exposure opportunities. Your manager can only guide and advocate for you if they know what you want and how you see your next steps.

Understand the Next Level

Wanting a promotion isn’t the same as being ready for one. Ask yourself and discuss with you manager what someone at the next level consistently does, where you are already operating at that level, and what skills you should focus on next. Then focus on developing those skills — not just doing more work. This step is about gathering intelligence on what it takes to get to the next level. You can ask your manager, but also seek perspective from colleagues and more senior people across the organization.

At senior levels, promotion often comes with a fork in the road: the manager path or a higher-level individual contributor path. Choosing intentionally matters. Ask yourself what energizes you, what you are curious to try next, and which responsibilities you want more or less of. You can always switch later, but drifting blindly is costly.

Exposure and Finding Advocates

Promotion decisions are often made by people who don’t work with you day to day, so visibility matters. Seek opportunities to lead cross-team initiatives, own high-impact problems, present your work, and mentor colleagues or interns. Your manager is important, but rarely the only voice — advocates across the organization can give honest feedback, validate your impact, and argue your case when you’re not present. Building these relationships requires time, trust, consistency, and visibility, not last-minute favors.

Soft Skills Are the Differentiator

At senior levels, promotions are rarely blocked by technical skill. They’re blocked by communication, trust, influence, and how confident and safe people feel working with you. Amazing work is meaningless if people don’t see or understand it — especially when your manager and advocates are making the case behind the scenes. Developing these soft skills is often the deciding factor between being recognized or overlooked.

Building Skills Through Mentoring and Programs

Mentoring — even informally — is a powerful way to practice leadership, multiply your impact, and demonstrate seniority before holding any formal title. Participating in workshops or internal training programs helps you build targeted skills, signal growth intent, and meet people outside your immediate circle. Many of the most valuable connections could come from these programs, giving you both the skills and the visibility needed to advance.

Growth doesn’t always come with a title change

Some of the most meaningful progress happens through lateral moves, taking on new responsibilities, or expanding your scope within your current role. Even within a same band or level, there’s room to grow and stretch — and that’s normal. Focus on building your skills, visibility, and influence, and make sure you’re increasing your standing within the band. Be patient, but not too patient — keep demonstrating impact, seeking feedback, and asking for what you want when the moment is right.

Keep a “Brag List”

Maintain a simple record of your wins, impact, metrics, feedback, and projects you’ve influenced. This isn’t ego — it’s being data-driven. Having this documentation makes it easy to show your impact when promotion time comes and ensures nothing is forgotten.

Be Pragmatic

Ideally, promotions are a byproduct of growth rather than the sole objective. That said, titles and compensation do matter — after all, most of us work for a paycheck.

Career growth is about working smartly, not just more. Sometimes, despite doing everything right, opportunities aren’t available internally, timing isn’t right, or politics and organizational factors block growth.

In those cases, explore internal mobility, have honest conversations, and recognize when moving on is valid. From experience, the biggest growth and salary bumps often come from changing companies — and that’s okay.

Timing and Life Priorities Matter

Not every season of life is a promotion season. Even for highly driven people, there are times where more responsibility simply isn’t what you want — and that’s okay.

I’ve had phases where I was passionate about my work but intentionally didn’t want additional scope due to other life priorities.

This guide is not meant to make you feel guilty if you’re not chasing a promotion right now. Growth isn’t linear. Ambition isn’t constant. Both are human.

Final Thought

Working hard is only part of the story — promotions come when you’re intentional about your goals, make your impact visible, and aren’t afraid to ask for what you want. Those who move fastest take charge of their careers, back their work with evidence, and carry a sprinkle of luck along the way. Success isn’t just effort — it’s strategy, courage, and timing.

So, what’s your next move? Make it count.

Offboarding isn’t just about documents, models or dashboards — it’s about people, relationships, history, and continuity, ensuring the team and ongoing work thrive after you step away.

Note: This guide is most relevant when an incoming leader has been appointed. If no direct replacement exists, much of this knowledge should be shared with an interim manager(s), distributing technical details across the team as needed to ensure continuity.

People: Supporting and Empowering the Team

A team is more than a collection of “direct reports” — it’s the people, their talents, working styles, and collaboration that make everything possible. During the transition, my work with the team and new manager focused on three following areas.

I provided the incoming leader with background on the team, including individual strengths, ways of working, and key dynamics. This helped them step in with context and confidence, understanding both operational priorities and the human side of the team.

I also spent time in one-on-one conversations with team members to reflect on our work together, share constructive feedback, and hear their perspectives. These conversations focused on what they valued in our collaboration, the challenges they faced, and their personal and professional development.

Together with the new manager, we aligned on priorities, expectations, and the handover of responsibilities. We facilitated a session on career goals, helping the team articulate aspirations and development opportunities, and shared stakeholder feedback to provide context on the impact of their work. A key principle was career continuity: everyone should know their growth, development, and opportunities would not be impacted by a change in management. We also held three-way meetings to review plans for the next couple of quarters, ensuring smooth continuity while making the transition supportive and human, not just operational.

Stakeholders: Passing Trust and Context

Stakeholders are critical to a team’s success, providing guidance, resources, and alignment with broader business goals. I documented key relationships, priorities, and past decisions, and created a list of go-to contacts for the incoming leader.

I suggested which stakeholders to engage first to build early alignment and trust, both during the handover process and after I had left. For some, we arranged introduction meetings; for others, introductions were made via message. The goal wasn’t just to hand over names — it was to transfer relationships, context, and institutional knowledge that keeps work moving smoothly.

Business Context: Showing the Past to Inform the Future

Understanding the broader business context is essential for any incoming leader. I shared key milestones, dashboards, frameworks, and stories of prior initiatives and lessons learned. The goal was to provide context without overwhelming with detail.

By framing history as a guide rather than a burden, the incoming leader could understand why past decisions were made and navigate future challenges thoughtfully — ensuring continuity while leaving space for their own approach.

Projects & Portfolio: Completing, Not Starting

During the transition, I deliberately avoided taking on new responsibilities, focusing on wrapping up ongoing projects. Initiatives were completed, operationalized, or documented for continuity.

For projects that were still in progress or required further attention, I ensured they were clearly documented with ownership, context, and next steps, so the incoming leader could pick them up without uncertainty. This included outstanding decisions, dependencies, and any risks to monitor.

This approach ensured the incoming leader could step in without half-finished work or confusion about priorities, while leaving a transparent view of unfinished business for them to manage. It provides a clean slate to prioritize new initiatives while maintaining continuity on ongoing work.

Future Ideas & Opportunities

Passing the torch also means sharing ideas for the future. We brainstormed next steps which could be organized ideas into four categories:

• Short-term / Already Agreed: Initiatives with momentum or executive alignment.

• Quick Wins: Small, high-impact improvements that deliver immediate value.

• Incremental Improvements: Medium-term refinements to processes, models, or workflows.

• Long-term / Strategic Impact: Aspirational opportunities requiring research, buy-in, or resources.

This provides context for decision-making while leaving room for the incoming leader to set priorities and bring their own perspective.

Technical & Systems Context

My work and my team’s work involved building and operationalizing production systems. During handover, I reviewed and filled gaps in the documentation of technical workflows, operational processes, and monitoring practices, while highlighting ongoing priorities.

The focus was on gradual ownership — providing resources and context so the incoming leader could manage systems effectively without being overloaded. Recordings, walkthroughs, and curated documentation helped them absorb critical knowledge sustainably.

Reflection

Leaving is naturally a time for reflection — a pause to look back, make sense of what was built together, and acknowledge the people behind the work. I’ve always tried to catch leavers before they go, and as I prepared to leave myself, I wanted to do the same: create space to appreciate contributions, revisit key moments, and share a few laughs (and shed a few tears) over past challenges.

In the weeks leading up to my departure, I spent time with colleagues and stakeholders in informal conversations, focusing on:

• Listening to what they valued in our collaboration, the challenges they experienced, and their broader goals.

• Sharing my own reflections on teamwork, processes, and cross-functional collaboration.

• Looking back on successes, missteps, and the lessons we learned along the way.

It was a bittersweet moment — one that allowed space to honor achievements, reflect on growth, and appreciate the relationships that made the work meaningful.

Conclusion: Passing the Torch with Purpose

Offboarding is not just administrative — it’s a final act of leadership. By focusing on the team, stakeholders, business context, projects, future opportunities, reflection, and structured handover practices, a leader can leave the incoming leader confident, the team empowered, and ongoing work resilient.

Leadership isn’t only about what you accomplish in a role — it’s about how you enable the next person to succeed. Done well, offboarding is passing the torch with care, pragmatism, warmth, and foresight, while leaving space for new ideas, improvements, and growth.

It’s that time of year when feedback is everywhere: reviews, surveys, casual check-ins. And yet, giving and receiving it well remains one of the hardest skills to master. I’ve been reflecting on my journey as a data leader, recalling moments when feedback went horribly wrong and others when it sparked real growth.

Books like Radical Candor and The Culture Map have shaped my approach, but nothing beats learning from real experiences: as an intern caught off guard by months-old feedback, as a leader asking colleagues for candid thoughts, and as someone navigating international teams with diverse communication styles. Over the years, I’ve noticed that with age and experience, I’ve become both more confident in giving feedback and more diplomatic in its delivery.

Feedback is a gift. It is one of the most powerful tools we have as leaders and teammates, but only when it is done thoughtfully: with honesty, listening, and trust. When these conditions are met, even difficult conversations feel safe rather than threatening. These lessons extend beyond work; in personal relationships, nobody wants defensiveness, lack of appreciation, or frustrations silently building up. Being kind, direct, and honest matters everywhere.

I’m not going to talk about ratios of positive to negative comments or tricks to manipulate feedback. This is about genuine, human connection and learning from each other.

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🤝 Principles for a Strong Feedback Culture

The best feedback systems are built on human connection and a shared mindset, not rules.

• Be Kind, Direct, and Open: Honesty and empathy go hand in hand. Feedback is only useful if it is clear, actionable, and understandable. Always focus on your intention: be genuine and add value, whether helping someone improve, boosting their confidence, or offering meaningful praise. Focus on being kind, not just nice — honesty is essential for growth and trust.

• Assume Good Intentions: Approach feedback with a positive, curious mindset. Assume it is meant to help you grow and improve relationships and processes. When I give feedback, it is always because I care about the person and their success. Mixing perspectives is why feedback is essential.

• Build Trust: People give and receive feedback more openly when they feel safe and heard. Building trust requires time, consistency, and empathy. Showing genuine care for both the work and the person makes all the difference.

• Be Mindful of Cultural and Personality Styles: Coming from a Slavic background and living in the UK, I’ve learned that communication styles vary widely. Some people, like me, are pretty direct, while others prefer subtle framing. Being mindful of differences, asking clarifying questions, and adapting without taking offense is essential for international teams.

A Cautionary Tale: Over a decade ago, I gave my manager a piece of feedback: “I think you made a mistake when you ran xyz experiment.” It was factual and private, yet my delivery was too blunt. The feedback ruined our relationship. I learned that cultural competence is as vital as factual correctness.

• Iterate and Practice: Feedback is a skill. I frequently ask my team for input on how I communicate, run meetings, and handle pressure. The more you practice, the more natural giving and receiving feedback becomes.

• Don’t Be Stingy with Positive Feedback: Positive feedback is more than a checkbox. Treat it as a compliment everyone loves. Give it to the person, to the group, or to the team, and keep it public when appropriate. Give praise when it is genuine, specific, and timely. Recognizing contributions openly strengthens relationships and motivation.

🗣️ The Art of Giving Feedback

Focus on Timeliness and Depth

• Give Small Feedback Often: Frequent, small feedback prevents surprises and builds trust. Celebrate wins and address small issues before they grow. Highlight specific achievements and extraordinary contributions.

• Never Say “Not Now”: Asking for feedback is a vulnerable act. Saying “not now” quietly erodes trust. If you need time, schedule a follow-up and ensure it happens promptly.

• Go Deep, Not Fast: Constructive feedback works best when you take time to give examples, explore motivations, and discuss patterns. Feedback should feel like a conversation, not a grenade thrown unexpectedly.

• Co-Create Improvements: Rather than dictating solutions, collaborate on next steps. Ownership accelerates growth and makes it lasting.

Deliver with Respect

• Start With Their Reflection: Invite reflection first. Often, people surface key insights themselves, making feedback more meaningful. Coach them while thinking aloud together.

• Give Feedback Across the Organization: Feedback is not just top-down. Provide it to your direct reports, peers, and managers. Always be kind, specific, and timely.

• Pick the Right Channel: Sensitive topics require in-person or call conversations, where tone and body language matter.

• Avoid Anonymous or Drive-By Comments: Vague feedback is frustrating and unhelpful. Real feedback needs context and room for clarification.

👂 The Skill of Receiving Feedback

Adopt a Growth Mindset

• Treat Feedback as a Gift: Feedback is people showing they care — about you, your work, or your team. Even if you disagree, there is almost always something valuable to take away. I ask for feedback regularly via informal channels. I especially value feedback from leaving colleagues, who often provide candid insights without fear of retribution.

• Reflect, Don’t React: Step back and process the conversation. Immediate emotional reactions rarely help. Staying calm, mature, and grounded allows for thoughtful responses.

Navigating Difficult Moments

• Handle Emotional or Venting Feedback Calmly: Sometimes feedback comes as a vent. Let people speak, validate their feelings, and explore the root cause. Creating space for honesty strengthens trust and makes future conversations easier.

• Handle Defensive or Harsh Responses Calmly: Defensiveness happens. When people are harsh, I focus on listening and staying open, while standing my ground. Understanding my own intent and assuming good intent from others allows for better outcomes.

• Appreciate Feedback: Providing feedback takes effort. People open up, navigate friction, and invest time to help you grow. Acknowledge their effort and celebrate wins.

• Learn From Timing: One of my worst feedback experiences was as an intern. I thought everything was going well, only to receive feedback months later that caught me off guard. Timely feedback allows reflection, learning, and course correction.

🚀 Final Thoughts: Earn Your Credits

Feedback is both an art and a skill. You can blend directness with diplomacy and genuine care, creating a positive impact. Be the leader who models the behavior you want to see: ask for it, give it, and receive it as a gift.

Strong feedback cultures do not grow from rules. They grow from repeated, caring interactions.

Start by giving and receiving feedback with people you already trust — you have trust credits to spend there. Critically, make time for it. These intentional conversations are what strengthen relationships everywhere.

What’s your approach to giving or receiving feedback? I’d love to hear your experiences and lessons in the comments.

“You don’t need to be a coding prodigy — you just need curiosity, persistence, and a practical mindset.”

When people ask how I got into data science, they often expect a checklist: “Learn Python, build a portfolio, apply for jobs.”

But honestly? What got me hired wasn’t a certificate — it was curiosity, initiative, and a willingness to learn fast.

I didn’t start out as a “data scientist.” My background was in Chemical Physics, followed by a computational PhD, so I already knew some programming — just not in Python or SQL.

My path into data science was built one deliberate step at a time: squeezing every bit of value out of what I already knew and learning what I didn’t.

But it wasn’t a straight path.

My PhD was going well — I loved science and problem-solving — but I eventually realized that academic life wasn’t for me. I wanted to apply my numerical and analytical skills to real-world problems, do work that created tangible impact, and build a well-rounded, stable career outside academia.

So, I made the tough decision to leave my PhD and transition into industry. It wasn’t a failure — it was a redirection. Looking back, it was one of the best career decisions I’ve ever made.

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🧠 Step 1: Build the Right Foundations

If you’re starting from scratch, focus on coding fundamentals and data fluency, not memorizing syntax.

I already had experience with other programming languages, but I wanted to think like a data scientist — to explore, manipulate, and communicate insights from data.

I used Jose Portilla’s “Python for Data Science and Machine Learning Bootcamp” on Udemy to build my Python foundations from the ground up.
I paired that with consistent SQL practice, using open datasets and small projects to get comfortable writing queries and joining tables.

Before you worry about deep learning or neural nets, make sure you can:

• Write clean, logical code.

• Query and combine data effectively.

• Visualize and explain your insights clearly.

• Articulate the meaning and the conclusions and impact of your work.

It’s not about mastering everything. It’s about developing the confidence to ask questions of data and find answers yourself.

🎧 Step 2: Obsess Over Something

Once I had the basics, I became obsessed with recommendations — because I saw a BBC job ad for a role focused on recommendation systems.

I was fascinated by the idea that algorithms could shape how millions of people discover music, podcasts, and stories. So I set out to learn everything I could.

I read about what the BBC data team did, what problems they solved, and how they thought about personalization. I devoured Medium articles on collaborative filtering, content-based recommendations, and hybrid models.

I was also fascinated by the ethical dimension of machine learning— how to recommend responsibly, avoid bias, and respect diversity — something the BBC takes seriously.

I learned what metadata actually meant and why it’s so critical to discovery.
And I bought Frank Kane’s “Building Recommender Systems with Machine Learning and AI” to get hands-on experience.

For a few weeks, I completely immersed myself. I read, coded, experimented, and absorbed context until I could explain — in my own words — how a recommender system really worked.

That obsession paid off: it prepared me for the BBC interview and helped me think like the team before I even joined.

💡 Step 3: Develop a Practical Mindset

What actually got me the job wasn’t perfect technical knowledge — it was a practical mindset and the ability to learn the right things incredibly fast.

I could show that I:

• Understood the user problems and friction points the team was tackling.

• Connected data work to meaningful, human outcomes.

• Was curious and proactive about solving problems.

• Had already shown initiative in learning the space independently.

In interviews, I didn’t try to impress with jargon. I explained how I approached learning something new, what I’d explored about recommender systems, and how I applied that knowledge in context.

Managers don’t expect you to know everything — they look for people who’ll figure it out.

🧩 Step 4: Learn the Rest on the Job

The truth is: I learned most of my data science on the job.

At the BBC, I moved from theory to production — building recommendation engines and NLP systems for BBC Sounds, helping millions of listeners discover the right content at the right time.

Later, at Trustpilot, I went further — working on lead scoring, pricing optimization, and forecasting, before becoming Data Science Manager for Applied AI.

That’s where I learned the real-world side of machine learning:

• How to deploy models in production.

• How to use Docker, CI/CD pipelines, and Google Cloud (BigQuery, Vertex AI).

• How to operate at scale — and learn at a crazy pace while doing it.

And somewhere along that journey, I discovered something I truly love: Explainable AI.

I became fascinated by the intersection of technology, business, and people — how explainability bridges all three. It’s where technical rigour meets trust, and where machine learning becomes meaningful.

I started implementing frameworks like SHAP to make models transparent, helping non-technical stakeholders understand why the model made a decision. Watching trust build and impact grow from that clarity was a turning point for me.

That’s when I realized: this is where I’m meant to be. It’s impactful work — and I’ve never looked back.

💬 Step 5: Keep Growing (Soft Skills Matter Too)

As I’ve grown into leadership, I’ve realized that soft skills are what truly make great data scientists stand out.

• 🌱 Attitude over aptitude: curiosity beats credentials.

• 💬 Communication: explain ideas simply — that’s influence.

• ❤️ Empathy: understand your users and stakeholders deeply.

• 🤝 Collaboration: the best outcomes happen in diverse teams.

Technical skills open the door. Soft skills build the career.

I also learned to build and fail fast, to iterate instead of perfect, and to deliver value first, then improve — lessons that still shape how I lead teams today.

Because data science isn’t just about AI, models or code; it’s about people, context, and impact.

⚡ Step 6: Squeeze the Most Out of What You Already Know

I didn’t start from zero.
I squeezed every ounce of value out of my Physics degrees, computational PhD, and every extra course I took along the way.

Then I packaged that experience strategically in my CV, focusing on transferable skills — data analysis, problem-solving, modeling, and research design.

That combination of technical foundation and applied curiosity made my transition into data science possible.

(I’ll dive deeper into exactly how I translated my academic and research background into a strong data science CV in an upcoming article.)

🚀 Final Words

There’s no single, perfect route into data science.
Mine involved chemistry labs, simulation code, online courses, and an intense fascination with making an impact.

But that’s what makes this field so exciting — it’s open to anyone who’s curious enough to keep learning and brave enough to start small.

If you stay curious, keep learning, and focus on solving real problems for real people — you’ll get there.

And if you’re at a crossroads like I was — wondering whether to requalify and start in tech — know that it’s okay to choose a different path from the one you originally set out on.

Sometimes, the best experiments happen outside the lab.

💬 If you enjoyed this, please subscribe for more reflections on my data journey — building practical, human-centered AI, and lessons from leading data science teams at scale.

✍️ About the Author

Asia Faulds is a Data Science Manager at Trustpilot, leading the Applied AI projects across product, go-to-market, and operations.
With a background in Chemical Physics, a computational PhD, and hands-on experience at the BBC and Procter & Gamble, she’s passionate about building explainable, human-centered ML systems that sit at the intersection of technology, business, and people — creating solutions that are both trusted and transformative.

When I applied for my first data science role, I wasn’t the obvious candidate. I was 1.5 years into a computational PhD, juggling teaching, tutoring, and part-time jobs — but I had a first-class degree in Physics, strong analytical skills, and an applied, problem-solving mindset.

I didn’t have research papers or Kaggle medals. What I did have was curiosity, drive, and a CV that spoke the language of impact.

And here’s the fun part — I’m showing real screenshots from that original CV, exactly as it looked six years ago! No polishing, no rewriting — just what I actually sent when applying for my first data science job.

Here’s how I built it, and what made it work.

❗️Before We Start — What Nobody Tells You

A degree alone — even a good one — isn’t enough.

For subjects like physics, maths, or chemistry, which are more theoretical, employers want to see how you’ve applied that knowledge.
It’s different from a field like dentistry, where the degree itself is the job.

It was a bitter pill to swallow at first — realising that companies care about talent, but talent that works for them.

Initiative matters. You need to show how you can turn theory into impact — through projects, internships, or online learning — and prove that you can apply what you know to solve real problems.

🎓 1. I Focused on My Strengths, Not My Gaps

Leaving a PhD halfway through wasn’t an easy choice, but it was the right one for me. I realised I wanted to apply my skills beyond academia — to build things that created tangible value, not just publishable results.

So I focused on what I did have:

• A strong academic foundation in physics, maths, stats and chemistry

• A first-class degree

• An industrial internship

• Evidence of learning fast and delivering independently

My summary was clear and confident:

It didn’t oversell. It showed intent, drive, and initiative — the things that actually matter.

🧠 2. I Translated My PhD Into Data Science Language

My PhD in computational chemistry wasn’t “machine learning,” but it was still deeply technical and analytical. I was already:

• Writing and debugging code

• Analysing massive simulation outputs

• Designing experiments

• Communicating results clearly

So I rewrote that section of my CV using data science language:

That made my work instantly relatable to hiring managers who had never seen my research area before. I reframed technical research into clear, data-driven achievements.

⚙️ 3. I Showed Every Bit of Relevant Coursework

I squeezed out everything relevant from my degree — coursework, tools, even small coding projects. If it demonstrated quantitative thinking or data handling, it went in.

From my Master’s degree, I included:

• PCA and data modelling

• Monte Carlo methods

• A Java simulation project

💬 4. I Highlighted Transferable Skills (and Drive)

I wanted to show I wasn’t just technical — I could communicate, collaborate, and deliver.

Throughout my CV, I gave examples of:

• Communication: presenting to academic and industrial audiences.

• Teamwork: collaborating across departments.

• Organisation: balancing research, teaching, and tutoring.

• Independence: leading projects and solving problems solo.

• Adaptability: picking up new tools quickly.

And I was honest about where I was in my journey. If I was still learning ML techniques, I said “Familiar with supervised learning algorithms and exploratory data analysis.”

Nobody’s perfect — but progress counts. I made sure to celebrate every small step forward and show that I was learning fast.

Why it worked: balanced technical skills with evidence of communication, collaboration, and growth.

🧩 5. I Kept It Clean and Easy to Scan

I didn’t over-complicate the format. It was:
✅ 1–2 pages
✅ Clean and consistent
✅ Structured logically
✅ Easy on the eye

Hiring managers are getting hundreds (if not thousands) of CVs!!!
Most use keyword searches to narrow down relevant applications and skim for clarity and relevance. A clean CV isn’t about design — it’s about thinking clearly and respecting the reader’s time.

🧰 6. I Showed Real-World Experience and Ownership

My internship at Procter & Gamble gave my CV real-world credibility.
I didn’t list tasks — I showed impact and independence:

This section showed that I could own projects, deliver outcomes, and work at pace — key traits for any data scientist.

✉️ 7. I Reached Out and Showed Initiative

I didn’t just apply and wait. I found the company’s Data Science blog, read about the team, and learned what they were building.
Then I emailed a hiring manager directly — with a thoughtful cover letter and my CV attached.

I explained what I’d been learning, why their work inspired me, and how I could contribute.

That small act of initiative — reaching out, showing context, and connecting the dots — made all the difference.

👀 8. Why It Worked

Looking back, this CV worked because it was:
✅ Honest and authentic
✅ Focused on progress and curiosity
✅ Structured clearly and easy to read
✅ Balanced technical and soft skills
✅ Aligned with what the BBC needed

I didn’t pretend to know everything.

I showed that I was learning fast, taking initiative, and celebrating small wins — like becoming familiar with ML techniques or running my first experiments in Python.

That’s what hiring managers really look for — not perfection, but momentum.

🚀 Final Thoughts

If you’re trying to land your first job after university, remember:
You don’t need a perfect profile. You need focus, honesty, and a willingness to learn fast.

Show initiative.

Celebrate progress — even small ones like “familiarity with ML techniques.”
And keep translating what you already know into the language of impact.

Your CV is more than a list of experience — it’s a story of curiosity, growth, and momentum.

💬 If you enjoyed this

Follow for practical insights on data science careers — from early steps to leading with impact in applied AI.

✍️ About the Author

Asia Faulds is a Data Science Manager at Trustpilot, leading Applied AI projects across product, go-to-market, and operations.
With a background in Chemical Physics, a computational PhD, and hands-on experience at the BBC and Procter & Gamble, she’s passionate about building human-centered ML systems — and helping others translate their skills into real-world impact.