Data Scientist and ML Engineer | MSc Data Science @ University of Bristol (Russell Group, 2025) | AI/ML Research Support – NIHR-funded NHS Stroke Rehab | Ex-Unilever Data Science Intern | End-to-end ML & AI pipelines with measurable impact
Data Scientist & ML Engineer building production-grade, cloud-native ML systems: data pipelines, feature engineering, orchestration (Airflow), deployment, monitoring, drift detection, and governance. Hands-on across healthcare, industrial, and commercial domains — leveraging predictive modelling, time-series forecasting, NLP, deep learning, generative AI, and XAI (SHAP/LIME).
Tech Stack: Python, SQL, TensorFlow/Keras, Scikit-learn, Airflow, Docker, Azure/AWS, CI/CD, Flutter integration.
Impact: Process efficiency gains up to 40%, energy savings 12–15%, decision-ready clinical and operational insights.
📍 Swindon, UK, Data Scientist at BG Automotive
Passionate about AI for social good — developing production-grade AI for NHS stroke rehabilitation with personalized exercise recommendations, LLM pipelines, generative models, and user-centered co-design. Bridging ML research, software engineering, and clinical impact, with prior experience in cross-functional analytics at Unilever.
- MSc Data Science, University of Bristol (2024–2025) – Distinction track (AI, NLP, Bayesian methods, visual analytics)
- BTech Computer Science Engineering, DIT University (2020–2024) – First Class Honours (85.4%)
BG Automotive
Swindon, UK
Apr 2026 – Present (Full-time)
NIHR NHS Stroke Rehabilitation Project – University of Bristol
Apr 2025 – Apr 2026
- Led development of a production-ready clinical AI system for personalised stroke rehabilitation
- Built end-to-end ML pipelines (data → modelling → deployment → monitoring)
- Integrated LLMs, generative models, and explainable AI into a real-world healthcare application
- Designed GDPR-compliant cloud architecture (AWS/Azure, Airflow orchestration)
- Conducted co-design with clinicians & patients, translating feedback into model improvements
- Lead author on peer-reviewed research (HEALTHINF)
Unilever
2024
- Delivered 14+ ML & analytics solutions across supply chain, manufacturing, and safety
- Built optimisation system for 250+ SKUs, reducing changeover time by 40%
- Developed forecasting & anomaly detection systems improving efficiency by 15–20%
- Automated workflows (SAP + Python), scaling decision-making across operations
- Lead Author: Paper accepted at IPEC-ECCE Asia 2026, Nagasaki, Japan
- Lead Author: “Personalised Stroke Rehabilitation: An AI Pipeline for Exercise Programmes Using a Co-Designed Decision Support Tablet Application” — HEALTHINF 2025 (peer-reviewed, NIHR-backed)
- NHS Stroke Rehab AI App – Flutter + ML personalization, clinician/patient co-design, production deployment
- EMI Surrogate Modelling – Physics-informed neural nets, multi-head attention, R² >0.95, SHAP analysis (thesis)
- UK Census Visual Analytics – Bayesian imputation, UMAP/t-SNE/KMeans, interactive Tableau dashboards
- Peri-Operative Time-Series Forecasting – LSTMs/CNNs/Transformers, distributed GPU scaling
- Protein Residue Prediction (Sapienza) – CIRNet + feature engineering → 78% accuracy
- McKinsey Forward Program (2025)
- Bristol PLUS Award – Leadership & Employability
- BILT Student Research Festival – Highly Commended & Funding
- AWS Academy Graduate – Cloud & ML
- Infosys AI/NLP/Deep Learning/RPA
- Salesforce Superbadges
- NIHR ABI Dissemination Conference, London (2025)
- HDRN Workshop (2025)
- ETH Oxford Hackathon Participant (2025)
- Postgraduate Representative – Bristol Data Science Society
Professional Bharatanatyam dancer | Carnatic vocalist | Marathon runner — discipline, creativity & resilience fuel my work
✨ Open to graduate/junior-mid roles in Data Science • ML/AI Engineering • MLOps — healthcare, fintech, or impact-driven tech
Connect: LinkedIn | thrisharajkumar5@gmail.com