Open to R programming & clinical data roles · London

Raheem Malik

R Programmer/Clinical Data/Genomic Medicine

I turn raw clinical trial data into validated, CDISC-compliant outputs — SDTM, ADaM and TFLs in R — backed by a degree in Clinical Pharmacology and postgraduate study in Genomic Medicine at Imperial College London.

View my work GitHub LinkedIn
raheem.R
# raw trial data → submission-ready outputs
library(pharmaverse)

raheem <- raw_data() |>
  sdtm(pkg = "sdtm.oak") |>
  adam(pkg = "admiral") |>
  tfl(validated = TRUE) |>
  qc(traceable = TRUE)

hire(raheem)
#> [1] "Ready when you are."
6
module CDISC
programming portfolio
12
toxicity endpoints
modelled in ML pipeline
CRO
industry experience
at Fortrea
Imperial
PGDip Genomic
Medicine, 2026
01About

Clinical pharmacology meets reproducible code.

I'm a postgraduate Genomic Medicine student at Imperial College London who programs in R for clinical trial data — building SDTM and ADaM datasets, tables, listings and figures, and a purpose-built R package for descriptive statistics.

My background in Clinical Pharmacology and a CRO internship at Fortrea mean I understand both the science behind the data and the regulated environments it lives in. I've also built predictive models and a Streamlit web application, and I care about work that is reproducible, well-documented and easy to trust.

BasedLondon, UK
FocusClinical & statistical programming
Core stackR · Python · SQL · SAS
StandardsCDISC · SDTM · ADaM
StudyingPGDip, Imperial College
02Technical skills

</>Languages

RPythonPandasNumPyScikit-learnSQLSAS (foundational)

{ }Clinical programming

SDTM · sdtm.oakADaM · admiralTFL / TLGCDISC CTISO 8601gtsummaryR packages

σStatistics & methods

Clopper-Pearson CIsForest plotsPCALogistic / Bayesian regressionNeural networksDecision treesROC-AUCGene set enrichmentNCA

⚙Tools & practice

StreamlitFastAPIGit / GitHubJupyterReproducible pipelinesQC & validationTraceabilityCross-validation

⚕Clinical & scientific context

CDISC clinical trial dataPharmacokineticsPharmacogenomicsTranscriptomics / RNA-seqRegulated, documentation-focused environments
03Selected projects

Clinical Statistical Programming Portfolio

2026

An end-to-end CDISC workflow, from raw data to validated tables and figures.

github.com/r4h3mm
  • Built the SDTM DS domain with correct controlled-terminology mapping and ISO 8601 dates — catching and fixing silent errors that had propagated NA study-day values.
  • Derived USUBJID and study-day keys consistent with the DM domain for traceable, join-safe subject data.
  • Produced a validated AE summary by system organ class and arm with tbl_hierarchical(), using the safety population as denominator and reconciling against the prior version.
  • Created severity bar charts and Clopper-Pearson forest plots, all version-controlled.
RCDISCadmiralsdtm.oakgtsummarylubridate
01Descriptive stats packagedescriptiveStats
↓
02SDTM · DS domainsdtm.oak
↓
03ADaM · ADSLadmiral
↓
04AE tables & listingsgtsummary
↓
05Figures & forest plotsggplot2

Transcriptomic Predictors of HCV Treatment Outcome

2026

Postgraduate research · baseline RNA-seq from a hepatitis C clinical trial

  • Finding gene-expression signatures that separate sustained response from treatment failure.
  • Pathway and gene set enrichment to characterise immune and cell-cycle processes.
  • Building predictive models on baseline transcriptomic features.
RRNA-seqGSEA

Molecular Toxicity Prediction Pipeline

2026

Supervised ML across 12 toxicological endpoints

  • Molecular fingerprint features with scaffold-based splitting to prevent data leakage.
  • Logistic regression baseline vs. multi-task learning, evaluated with ROC-AUC.
  • Documented model selection for transparency and reproducibility.
PythonScikit-learnROC-AUC

ACMG Variant Classification App

2026

Deployed rule-based clinical decision system

  • Implements formal ACMG variant classification guidelines as structured, traceable logic.
  • Modular evidence mapping that mirrors regulated clinical decision workflows.
PythonStreamlitGit

SGLT2 Inhibitors in High-Risk Diabetic Patients

2024

Clinical data analysis · awarded First Class

  • Cleaned and quality-checked large clinical datasets for diabetic cohorts.
  • Statistical modelling of therapeutic efficacy with a clear, reproducible method.
SPSSClinical dataQC
04Experience

Clinical Data Analyst

2024
City, St George's University of London · SGLT2 inhibitor project
  • Synthesised and cleaned large-scale clinical datasets with quality-control checks for data integrity.
  • Evaluated therapeutic efficacy with statistical modelling; final report awarded First Class.

Machine Learning Project Lead

2024
City, St George's University of London
  • Led a team analysing clinical datasets with PCA, comparing neural networks and decision trees in R and SPSS.
  • Justified model choice through ROC curves and forest plots for rigour and interpretability.

Data Analyst Intern

2023
Fortrea · Contract Research Organisation
  • Collated and analysed patient-experience datasets in a compliance-focused CRO environment.
  • Presented statistical findings to senior supervisors, influencing internal clinical-trial data management approaches.

Pharmacokinetics Analyst

2023
City, St George's University of London
  • Automated non-compartmental analysis of raw PK data in R, deriving clearance and half-life.
  • Wrote reproducible, documented analysis scripts.
05Education & certifications
Sept 2025 – Sept 2026

PGDip Genomic Medicine

Imperial College London

Bioinformatics, clinical genomics, pharmacogenomics and large-scale data analysis. Research project on transcriptomic predictors of HCV treatment outcome.

2022 – 2025

BSc Clinical Pharmacology · 2:1 Hons

City, St George's University of London

Advanced Data & Statistics, Advanced Pharmacokinetics, Advanced Disease & Drug Targets.

✓
Hands-On Clinical Reporting Using RCompleted
Raw data → SDTM → ADaM → TLGs with pharmaverse (admiral, metacore, tern, teal)
Genentech
◐
Genomic Data ScienceIn progress
Computational tools and workflows for genomics data
Johns Hopkins
◐
Data Scientist Career TrackIn progress
Supervised & unsupervised learning, feature engineering, Python, SQL
DataCamp
✓
Python for Data ScienceCompleted
Data cleaning, visualisation and predictive modelling
Codecademy
✓
Generative AI for HealthcareCompleted
Multimodal LLMs and prompt engineering in clinical contexts
Google

Let's talk data.

Looking for R programming, statistical programming and clinical data roles. I'd love to hear from you.

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