Data Scientist (Insurance) (KTP Associate), SMSAS

University of Essex; United Kingdom; On-site; GBP 25,000 - 35,000; Full time

Job summary

A prominent UK university is seeking a KTP Associate to analyze historical insurance data and support pricing strategies. This role offers an opportunity to work closely with Comfort Insurance, develop predictive models, and may lead to a PhD. Candidates should have a MSc in Data Science and experience with data analysis tools such as R and Python.

Benefits

Personal development budget of £4667

Management training and mentoring

Full access to university resources

Qualifications

• Practical and theoretical knowledge of computational intelligence/machine learning algorithms.
• Experience of complex data handling and management of large data sets.
• Strong knowledge of both R and Python data science packages.

Responsibilities

• Analyze, clean and process historical data.
• Develop an internal predictive risk model tool.
• Contribute to drafting academic papers and case studies.

Skills

Analytical skills

Problem solving

Communication skills

Interpersonal skills

Time management

Education

MSc in Data Science or related disciplines

PhD in Data Science or related fields

Tools

R

Python

caret

h2o

Numpy

Scikit-learn

Job description

KTP

Knowledge Transfer Partnerships (KTPs) are a unique UK-wide activity that help businesses to improve their competitiveness and productivity by making better use of the knowledge, technology and skills within universities, colleges and research organisations.

Further information is available at: https://iuk-ktp.org.uk/

THE PROJECT

The University of Essex is pleased to be working with Comfort Insurance to find a qualified graduate, with the right skills, to examine historical insurance data and draw insights which support optimised pricing and marketing strategies.

Subject to discussion and approval there may be the opportunity for the successful candidate to complete a PhD.

DUTIES OF THE POST

The duties of the post will include:

• Analysing, cleaning and processing historical data
• Processing large and complex volumes of data
• Researching models (such as GLMs, regression trees, random forest, etc) using historic data relevant to insurance claim prediction
• Testing models and evaluating performance across various algorithms, using company data
• Developing research and commercial objectives for the company related to improving pricing accuracy, optimising risk segmentation, enhancing claims prediction and target marketing/retention segments
• Calibrating models based on training/validation data sets
• Developing an internal predictive risk model tool
• Embedding technical knowledge into the company via presentations and workshops etc
• Developing proposals & recommendations to improve commercial outcomes
• Contributing to discussions with underwriters/other third parties to present & refine technical findings/proposals & supporting commercial recommendations
• Contributing to the drafting of academic papers and case studies
• Designing a profitability modelling framework
• Contributing to marketing strategies in support of commercialisation activity
• Developing a chatbot to identify common queries and capture customer preferences
• Participating in academic and/or industrial conferences and other events, to disseminate and present research outcomes to the wider community
• Publishing peer-reviewed articles in high impact journals in collaboration with academics at the University of Essex

These duties are a guide to the work that the post holder will initially be required to undertake. They may be changed from time to time to meet changing circumstances.

KEY REQUIREMENTS

Qualifications:

• MSc in Data Science, Statistics, Computational Actuarial Science, Computer Sciences or related disciplines.
• [ Desirable ] PhD in Data Science, Statistics, Computational Actuarial Science or related fields.

Knowledge and Experience:

• Practical and theoretical knowledge of computational intelligence/machine learning algorithms for predictive modelling and forecasting
• Processing and aggregation of heterogeneous data streams based on structured/unstructured data
• Practical and theoretical knowledge of mathematical and stochastic optimisation approaches
• Experience of complex data handling and management of large data sets.
• Strong knowledge of R and relevant data science packages (e.g. caret, h2o, mlr).
• Strong knowledge of Python and the relevant data science Python stack (Numpy, Scikit-learn, Scipy, Xgboost).
• [ Desirable ] An understanding of, or experience working in, the insurance and/or actuarial market would be highly advantageous.
• [ Desirable ] Knowledge and understanding of actuarial science, and risk.
• [ Desirable ] Knowledge of general insurance pricing methods.

Skills and Abilities:

• Excellent analytical, problem solving, communication and interpersonal skills.
• Ability to work to tight deadlines; excellent time management and organisational skills.
• Ability to clearly communicate & interact with people with commercial interests and from varied technical backgrounds.
• Commitment to continuous learning and adapting to new technologies and methodologies.
• Ability to maintain confidentiality when handling sensitive data.

BENEFITS

As a KTP Associate, the post will offer the following benefits:

• A personal development budget of £4667 (exclusive of salary).
• Management training and mentoring by an Innovate UK KTP Adviser.
• An interesting and challenging role, with exposure to a variety of stakeholders.
• Full access to university resources to complete the project.
• World-leading Academic and Company project supervision, with project support by a dedicated, sector leading KTP Office.

LOCATION

Comfort Insurance, Comfort House, 8 Goresbrook Road, Dagenham, Essex, RM9 6UR

Please see the attached job pack, which contains a full job description and person specification which outlines the full duties, skills, qualifications and experience needed for this role plus more information relating to the post. We recommend you read this information carefully before making an application. Applications should be made on-line, but if you would like advice or help in making an application, or need information in a different format, please contact [e-mail]

*More information: Working at the University