Senior Biomedical Data Scientist

The University of Manchester; Manchester; Hybrid; GBP 45,000 - 60,000; Full time

Job summary

A leading research university in Manchester is seeking a Senior Biomedical Data Scientist to lead computational analysis of omics data. You will develop computational pipelines, working collaboratively across institutions. The ideal candidate holds a PhD and has significant experience in machine learning and data analysis. This role offers a competitive salary and benefits, including a market-leading pension scheme.

Benefits

Market leading Pension scheme

Excellent employee health services

Exceptional annual leave entitlement

Paid closure during Christmas

Local and national retailer discounts

Qualifications

• PhD with significant computational and statistical elements.
• Expertise in analysis of omics data.
• Experience with machine learning.

Responsibilities

• Lead computational analysis of omics data.
• Co-develop a computational pipeline for multi-omic data.
• Work with a team to create tissue atlases.

Skills

Machine learning

Advanced statistical inference

Analysis of omics data

Leadership

Education

PhD in a relevant field

Job description

Overview

Applications are invited for the post of Senior Biomedical Data Scientist, based in the new Medical Research Council Centre of Research Excellence (MRC CoRE) in Exposome Immunology to be hosted jointly by the universities of Manchester and Oxford. The CoRE will leverage cutting edge computational approaches, novel experimental models, and experimental medicine studies to uncover how pollutants and infections interact with our genes to cause and exacerbate chronic inflammatory diseases.

Responsibilities

You will be the Manchester-based lead for computational analysis of omics data produced by the CoRE. You will co-lead development of a computational pipeline that integrates multi-omic data at the cellular level across exposomes and diseases, revealing novel biology and shared mechanisms of immune regulation. You will be an expert in the use of advanced statistical inference and machine learning methods for the analysis of omics datasets. You will work with a team of data scientists in Manchester and Oxford to create tissue atlases along with computational models to characterise the effect of exposures on different tissues in humans and model systems.

Qualifications

You will have a PhD with a significant computational and/or statistical element, expertise in analysis of omics data and experience of machine learning. A strong interest in biology is essential and you should be self-motivated, hard-working and able to lead a team.

Benefits

• Fantastic market leading Pension scheme
• Excellent employee health and wellbeing services including an Employee Assistance Programme
• Exceptional starting annual leave entitlement, plus bank holidays
• Additional paid closure over the Christmas period
• Local and national discounts at a range of major retailers

Equal Opportunity

As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

Working Arrangements

Our University is positive about flexible working – you can find out more here. Hybrid working arrangements may be considered.

Application Information

Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies. Any recruitment enquiries from recruitment agencies should be directed to [e-mail]. Any CVs submitted by a recruitment agency will be considered a gift.

Enquiries about the vacancy, shortlisting and interviews:

Name: Professor Magnus Rattray

Email: [e-mail]

General enquiries:

Email: [e-mail]

Technical support:

jobseekersupport.jobtrain.co.uk/support/home

Closing: This vacancy will close for applications at midnight on the closing date.

Please see the link below for the Further Particulars document which contains the person specification criteria.