03 · ABOUT

About

I'm a pharmacology student at King's who kept asking whether the answer would survive someone checking it.

The scariest spike I ever found turned out to be a wave of lawsuits, not a new side effect. It moved a share price and a legal strategy long before it moved a safety conclusion — and separating those three is the whole job.

That is the work I want to do: taking evidence that is genuinely uncertain and turning it into a decision somebody can act on and defend. Most people in drug safety aren't strong on data; most data people don't know drug safety; and the commercial question is usually asked by a third group who talk to neither. I'm trying to be useful at the join.

I'm looking for a summer 2027 internship in life sciences or economic consulting — the kind of work where the analysis has to hold up in front of a regulator, a court or a board.

The journey

  1. 2025

    Couldn't write a line of Python. Started learning data — in public.

  2. Early 2026

    First project: 20M+ adverse-event reports. Found a scary spike was litigation, not harm.

  3. Spring 2026

    SQL, patient records, and a signal detector validated on a known warfarin risk.

  4. Mid 2026

    ML, survival analysis in R, then AI reading medical text — the fanciest model came last.

  5. Late 2026

    Causal inference: a painkiller that looked twice as safe as it was, and two methods that caught it.

  6. Next

    Real clinical data, and the commercial and policy questions this evidence is actually built to answer — pricing, access, and what a regulator or a court will accept.

Skills & tools

Built alongside the degree, and used on every project below.

Data
Python (pandas, scikit-learn, matplotlib), R, SQL & SQLite
Methods
Causal inference (propensity scores, self-controlled case series), survival analysis, disproportionality signal detection, machine learning, NLP
Engineering
Git & GitHub, unit testing, GitHub Actions CI, Docker, CLI tools, Astro, Streamlit
Domain
Pharmacology, pharmacovigilance, real-world evidence, EHR data, SNOMED CT coding, openFDA / FAERS
Communication
Data storytelling for non-specialists, technical writing, public speaking

Education & activities

  • BSc Pharmacology (Hons), King's College London Expected 2028 · first-class marks in pharmacology and in research skills & statistics Year 2 includes Epidemiology & Population Sciences — study design, causal inference, validity, and the critical appraisal of methodology, data quality and interpretation. It is the formal version of what the projects on this site do informally.
  • Student Representative, BSc Pharmacology 2025–present · Staff–Student Liaison Committee Turned scattered complaints about a module into one clear signal — it reached the department, and the module changed.
  • King's100 Leadership & Employability Programme Selected, 2026 King's flagship leadership programme — selective, employer-led challenges.
  • Toastmasters International Member & Sergeant at Arms Regular public speaking; earned “Speech of the Meeting.”
  • Model United Nations Delegate Awarded Delegate of the Conference — research, debate and negotiation.

Beyond the work

Most of what I learn ends up explained somewhere — a Substack newsletter, project posts on LinkedIn, and short films on Instagram about how a drug's harm was first spotted. Explaining this work to people outside it is a skill I practise on purpose.

Away from the screen: building LIFTKCL, a beginner-friendly strength community at King's, and shooting photography — some of it below. London and Dubai; English and Arabic.

Through the lens

Photograph by Belal ZakyPhotograph by Belal ZakyPhotograph by Belal ZakyPhotograph by Belal ZakyPhotograph by Belal ZakyPhotograph by Belal ZakyPhotograph by Belal ZakyPhotograph by Belal Zaky

Happy to talk drug safety, real-world evidence, or how messy health data turns into decisions — reach me at hello@belalzaky.uk, or on LinkedIn and GitHub.