// MSc Applied AI · Microsoft & Neo4j Certified · Ex-Ericsson · Huawei · Priory Hospital
Leadership and data skills shown together — because in every environment, they were the same thing: data shaped the decisions, and leadership ensured the analysis was acted upon.
| Skill · Leadership | Evidence — Where It Was Built & How It Was Led |
|---|---|
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Data Analysis
↑ Led Teams On This
Python · SQL · KNIME · R · Pandas · Matplotlib
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Ericsson (Team Lead, 7 yrs): Led a team in systematic analysis of large-scale network datasets — directed the analytical workflow, owned outputs, reported findings upward to senior management at one of the world's largest telecoms companies. Huawei (4+ yrs): Coordinated end-to-end analytics workstreams across project management, field operations, and client teams — managing data quality and output standards. MSc Applied AI: Full analytical pipeline on structured clinical patient data — independently managed from data acquisition through feature engineering, model training, and interpretation. |
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Machine Learning
↑ Independent Research Lead
Scikit-learn · Classification · AUC-ROC · Sensitivity · Feature Engineering
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MSc Dissertation: Built and compared Logistic Regression, Decision Trees, and Random Forest classifiers to detect heart disease from clinical patient data. Evaluated via AUC-ROC, sensitivity, and specificity — with explicit analysis of what false negatives mean for real patients. Led the full research project independently — question through conclusion. Microsoft Azure Data Scientist Associate: Certified. Applied ML in cloud environments, model deployment, and responsible AI principles. Ericsson & Huawei: Applied predictive analytics operationally to inform where to direct engineering resources — ML in service of decisions, not just reports. |
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EHR / EDH & Clinical Data
↑ Governance Responsible
Electronic Health Records · NHS Systems · CQC Standards · ICD-10 · SNOMED CT
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Priory Hospital (current): Working daily with electronic health record and clinical documentation systems in NHS-referred inpatient services. Contributing to patient records — developing direct insight into how EHR data is created, where documentation inconsistencies arise, and how data quality at point-of-care determines what analysts can do downstream. Most health data analysts have never been inside this environment. MSc & Self-Study: EHR architecture concepts, electronic patient record structures, and health data standards. Feature engineering on clinical variables (cholesterol, BP, ECG) mirrors working with EHR datasets directly. CQC governance: Maintaining records to CQC standards — practical understanding of data integrity and audit trail requirements central to all regulated health data work. |
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Graph Databases
↑ Certified Professional
Neo4j · Cypher · Graph Modelling · Clinical Ontologies
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Neo4j Certified Professional: Certified in graph data modelling, Cypher querying, and database architecture — a capability most health data analysts do not currently hold. Clinical leadership relevance: Graph databases solve problems relational databases cannot: clinical knowledge graphs, drug-interaction networks, patient pathway analysis, SNOMED CT ontology mapping. Holding this positions for technical leadership at the frontier of NHS and pharma informatics — not just analyst work, but shaping the infrastructure other analysts will use. |
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Data Communication & Strategic Leadership
↑ 13 Yrs Executive Reporting
Executive Reporting · Power BI · Stakeholder Mgmt · Team Leadership · Mentoring
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Ericsson (Team Lead): Led engineering teams, mentored junior engineers, and produced executive-level performance reports for major telecoms clients — translating complex analytical findings into strategic recommendations under commercial pressure for 7 years. Huawei: Produced structured analytical outputs for senior management in a large multinational — developing the upward-reporting and data communication discipline that senior health analysts are expected to demonstrate from day one. MSc Engineering Management: Formal qualification in leading technical teams and communicating data-driven strategy to senior stakeholders in regulated technical environments. |
Most health data analysts understand EHR as a data schema. I understand it as a clinical workflow — because I have contributed to it.
Working within NHS-referred inpatient mental health services, I contribute daily to clinical documentation systems — electronic patient records, care plans, risk assessments, and observation records. This is not theoretical knowledge of EHR architecture. It is practical understanding of how data is entered, where inconsistencies arise, and what those inconsistencies mean for anyone trying to analyse the data downstream.
I have watched clinicians make decisions using incomplete electronic records. I understand why data quality at the point of care is not a technical problem — it is a human and workflow problem that analytical systems have to be designed around, not designed to ignore.
Through MSc Applied AI modules and structured self-study, I have developed working knowledge of EHR architecture concepts, electronic patient record structures, and clinical data standards including ICD-10, SNOMED CT, and OPCS coding systems. My dissertation work — feature engineering on structured clinical variables (cholesterol, BP, ECG) — directly mirrors the data preparation process when working with real EHR datasets.
For NHS analytics: Analysts who have worked inside clinical documentation systems understand data quality at source — not just data quality after it has been imported into a warehouse. This changes the questions you ask, the caveats you apply, and the recommendations you make.
For pharma & CRO: Clinical trial data management, CDISC standards, and EDC systems all require analysts who understand the gap between what a clinical protocol specifies and what actually gets documented. EHR experience from inside a ward directly develops that understanding.
For all health data roles: The ability to speak to clinicians about data quality — not just to data engineers — is a capability that almost no health data analyst brings from purely technical training. It is built from time on a ward.
I spent 13 years leading data-intensive engineering at Ericsson and Huawei — directing teams, owning analytical outputs, and translating data into decisions that kept large-scale infrastructure running. Data was never just something I produced. It was how I led.
In 2022 I relocated to the UK and completed an MSc in Applied Artificial Intelligence — applying machine learning to heart disease detection from clinical patient data. That project showed me the same rigour I had built in engineering could be directed toward something far more consequential than network performance.
To understand health systems from the inside, I took a role at Priory Hospital. I have worked within NHS-referred inpatient services, contributed to electronic health records, observed MDT meetings, and seen firsthand how data quality at the point of care determines what analysts can do downstream. Most health data professionals understand EHR from the outside. I understand it from the ward.
"The rarest thing in health data is someone who has seen it from both sides — the analytical and the clinical. I have been both."
I hold MSc Engineering Management, B.Tech Electrical & Electronics Engineering, Microsoft Azure Data Scientist, and Neo4j Certified Professional credentials. Target roles: NHS analytics, clinical data analysis, pharma, CRO, health informatics.
MSc dissertation applying ML to clinical patient data — with explicit clinical framing, not just statistical performance.
Applied supervised ML to detect heart disease from clinical patient data — working with cholesterol, blood pressure, resting ECG, age, and exercise response variables. Independently managed the full research pipeline from question design through model evaluation and written delivery.
Four real environments. Each one contributed a different dimension of the whole.
Writing at the intersection of health data, clinical AI, EHR systems, and what it means to lead teams through analysis that affects real people.
Actively seeking roles in NHS analytics, clinical data analysis, and pharma/CRO. If you are a recruiter, hiring manager, or fellow health data professional — I would genuinely welcome a conversation.
I bring a combination most candidates cannot: the technical depth of an AI graduate and certified data scientist, the leadership discipline of a 13-year engineering career, and the clinical perspective of someone who has worked inside NHS-referred services and contributed to electronic health records at the point of care.
Actively seeking positions in: