Research Assistant

London School of Hygiene & Tropical Medicine; Camden Town; On-site; GBP 39,000 - 46,000; Full time

Job summary

A leading public health university in London is seeking a talented Research Assistant to work on a project involving natural language processing in electronic health record data analysis. The position requires a relevant degree and experience in machine learning, with a salary range of £39,984 to £45,728 per annum. This full-time role is fixed-term for 18 months and offers generous annual leave and a pension scheme.

Benefits

Annual leave of 30 working days

Discretionary Wellbeing Days

Membership of the Pension Scheme

Qualifications

• Experience in machine learning with a focus on natural language processing.
• Strong analytical skills in data wrangling and programming.
• Excellent coding practices and version control experience.

Responsibilities

• Designing and implementing code-to-feature conversion pipelines.
• Developing and benchmarking NLP models for feature extraction.
• Conducting causal inference studies on electronic health record data.
• Disseminating findings through presentations and manuscripts.

Skills

Natural language processing

Machine learning

Data wrangling

Coding practices

Education

Relevant first degree in data science or a related discipline

Job description

Research Assistant

Department: Department of Medical Statistics

Salary: £39,984 to £45,728 per annum pro rata inclusive

Closing Date: Thursday 13 November 2025

Reference: EPH-MS-2025-12

The London School of Hygiene & Tropical Medicine (LSHTM) is one of the world’s leading public health universities. Our mission is to improve health and health equity in the UK and worldwide, working in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice.

We are seeking a data scientist with experience in natural language processing (NLP) methods to work on a research project exploring the use of NLP in causal studies using primary care electronic health record data. Typically, structured electronic health record entries are coded using standardized systems such as SNOMED‑CT and must be converted to features for analysis. The post-holder will develop deterministic algorithms based on code frequency and temporal patterns to generate binary features, and compare these with NLP‑based feature‑creation. Methods will be applied to real case studies, including an investigation of the relationship between inhaled corticosteroids and COVID‑19 outcomes using data from the Clinical Practice Research Datalink.

Key responsibilities include:

• Designing and implementing code‑to‑feature conversion pipelines.
• Developing and benchmarking NLP models for feature extraction.
• Conducting causal inference studies on electronic health record data.
• Disseminating findings through presentations and manuscripts.

Essential qualifications:

• Relevant first degree in data science or a related discipline.
• Experience in machine learning with a focus on natural language processing.
• Strong analytical skills in data wrangling and programming.
• Excellent coding practices and version control experience.

The role is full‑time (35 hours per week, 1.0 FTE) and fixed‑term for 18 months. It is funded by the Wellcome Trust and available immediately. Salary will be on the LSHTM salary scale (Grade 5, £39,984‑£45,728 per annum inclusive of London weighting). Annual leave entitlement is 30 working days per year, pro‑rata for part‑time. Additional discretionary "Wellbeing Days" are available. Membership of the Pension Scheme is offered. The post is based in London at LSHTM.

Applications should be submitted online via our jobs website by 10:00 pm on the closing date. The supporting statement must address each of the selection criteria in one or more paragraphs. The application will be considered incomplete if we receive minimal or generic responses such as "see attached CV".

We invite all qualified candidates, regardless of gender, ethnicity, disability, or other protected characteristics, to apply. LSHTM is an equal opportunities employer committed to an inclusive workplace where everyone feels respected, supported, and able to reach their full potential.