Bioinformatics & Aspiring Healthcare Data Science professional skilled in applying machine learning, statistics, and explainable AI to real-world problems in pharma, precision medicine, genomics, and clinical research. W…
Bioinformatics & Aspiring Healthcare Data Science professional skilled in applying machine learning, statistics, and explainable AI to real-world problems in pharma, precision medicine, genomics, and clinical research. With hands-on experience in Python, R, SQL, statistical modelling, QC workflows, metagenomics, and clinical datasets, I build data pipelines and evidence-based insights that support scientific and medical decision-making.
My project portfolio covers:
• Clinical ML & XAI: Built end-to-end ML and SHAP/LIME workflows for differential diagnosis using CBC data; deployed models using Flask.
• Genomics & Metagenomics: NGS/ONT data QC, host removal, and microbial profiling pipelines using Linux, Bash, Kraken2.
• Computational Biology: Conducted RNA-seq differential expression analysis (DESeq2) for cancer transcriptomics.
• Data Engineering Foundations: Data preprocessing, EDA, feature engineering, statistical validation, reproducible scripts.
I blend computational skills with strong biological understanding to create scalable, interpretable, and clinically relevant data solutions. I’m passionate about contributing to:
🔹 Pharmaceutical analytics
🔹 Clinical and real-world data science
🔹 AI/ML for diagnostics & drug development
🔹 Genomics and precision medicine
🔹 Healthcare innovation
Actively seeking opportunities in Data Science, Bioinformatics & Healthcare Analytics within pharma, biotech, and digital health, where I can contribute to impactful, patient-centric outcomes for improvised healthcare and precision medicine.
Built an automated Oxford Nanopore metagenomics pipeline: basecalling → adapter trimming (Porechop) → QC → host DNA removal → taxonomic classification using Kraken2 → summarized reports. Conducted sample QC, filtering, metadata merging, contamination detection, and downstream analysis for necrotic tissue samples.Collaborated with wet-lab teams to interpret microbial profiles for clinical relevance.Produced reproducible scripts, documentation, and standardized analysis reports.
Built ML models (RandomForest, AdaBoost, LightGBM) for clinical data classification (Vitamin B12 vs Folate Deficiency Anemia) using patient CBC data.
Applied statistical testing, EDA, feature selection.
Implemented feature engineering, model evaluation (accuracy, precision, recall, F1, MCC, AUC).
Performed hyperparameter tuning.
Designed experiments using cross-validation and GridSearchCV.
Used pandas, numpy, sklearn, matplotlib, and seaborn.
Achieved 99% accuracy.
Built REST API using Flask and deployed the best performing model locally.
Applied explainable AI (SHAP, LIME) for interpretability and transparency of the results.
Investigated RBC segmentation with U-Net (Python, OpenCV, TensorFlow).
Used data augmentation.
Used patch-wise training on 3D phase images for morphological analysis.
Conducted RNA-seq analysis of TCGA datasets to study MALAT1 expression across multiple cancers.
Applied statistical modeling and pathway analysis to identify biomarker and therapeutic potential.
Designed a responsive website using HTML and CSS.
Supported calculation of users BMI to determine their ideal calorie intake.
Used BeautifulSoup for webscraping/API-extraction in a separate project.
Built automated pipeline for Nanopore sequencing using Bash, Kraken2, Porechop, and Dorado.
Wrote reproducible workflows.
Generated QC dashboards.
Worked with Linux, HPC, and version-controlled workflows.
Worked as a project intern as a part of Masters thesis final dissertation to develop and deploy the machine learning model all the way from data collection and preprocessing till the deployment stage and model interpretability.Built ML models (RandomForest, AdaBoost, LightGBM) for clinical data classification (Vitamin B12 vs Folate Deficiency Anaemia) using patient CBC data. Applied statistical testing, EDA, and feature selection. Implemented feature engineering, model evaluation (accuracy, precision, recall, F1, MCC, AUC), hyperparameter tuning, experiment design (cross-validation, GridSearchCV), and used pandas, numpy, sklearn, matplotlib, and seaborn. The model performed very well with 99% accuracy.Built a REST API using Flask & deployed the best-performing model locally.Applied explainable AI (SHAP, LIME) for interpretability and transparency of the results. Tried to integrate RBC image-based morphological features for better classification.Deep learning: Investigated RBC segmentation with U-Net (Python, OpenCV, TensorFlow), data augmentation, and patch-wise training on 3D phase images for morphological analysis.Extracted shape descriptors, height–length curves, and statistical features for classification.Generated per-cell plots, CSV exports, and morphology profile visualizations.
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