Machine Learning Engineer

Job Type:

Permanent

Build a brilliant future with Hiscox
 

Position: Machine Learning Engineer

Reporting to: Lead Data Scientist

Band: II

Location: York - Hybrid

Introduction

We are looking for an experienced machine learning engineer to join a newly formed data science team. You will have the opportunity to shape the data science delivery pipeline by building the infrastructure to acquire data from the data platform, deploy models, maintain, monitor and upgrade core data science services in both Azure’s native platform and in databricks. You will be working closely with data scientists in a skills crossover methodology, and will contribute to the data science workflow, discussions and development, but still maintain a key function in the graduation of models from research into production. 

The role of the Machine Learning Engineer

This is an ideal role for an individual who is passionate about the use of data science to influence decisions and is keen to learn more about delivering value through the use of data.

Key Responsibilities

Ownership of the deployment framework for all data science services. You will have oversight of how data will flow into the data science life cycle from the wider business data warehouse

Oversight of the automation of the data science life cycle (dataset build, training, evaluation, deployment, monitoring) when we move to production

Good understanding of core data science principles and understanding of challenges of migrating research code into production code

Interest and ability to work closely with a team and collaborate on all aspects of the data science and deployment lifecycle

Work collaboratively with data scientists, data engineers and other technical people including pricing teams in order to help support maturation of analytics practice within the organization.

Writing high quality python code using industry best practice for model training and deployment

Required skills:

Strong python programming skills

Good knowledge of software engineering best practice

Experience with TDD (pytest or equivalent)

Experience with cloud native deployments (currently in Azure)

Experience with Databricks

Experience with managed endpoints, AKS or equivalent

Experience with VCS

Experience with CI/CD

Understanding / identifying opportunity to apply machine learning knowledge to solve business problems

Experience in developing predictive and prescriptive analysis (predictive modelling, machine learning or data mining) used to draw key business insights and clearly articulate findings for target audience

Desirable Skills

Graduate or Postgraduate qualification or equivalent experience in a relevant discipline e.g. engineering, mathematics, physics, statistics

Experience of data science in finance, insurance or Ecommerce is an advantage but not required.

Experience with neural networks and Tensor Flow, CatBoost, XGBoost, SKlearn, Pandas

Our technology

We are currently developing a new data platform in databricks that encompasses all our UK business unit’s data. This data will be managed and made available to the data science team to consume. The ML Engineer will be able to leverage this and extend it to realise full end to end data science services.

Rewards 

On top of a competitive salary, we also offer a wide range of benefits. 

25 days annual leave plus two Hiscox days

4 week paid sabbatical after every 5 years of service

Company and personal performance related bonus.

Contributory pension.

Other benefits include:

Money towards gym membership.

Christmas gift.

4 x life insurance.

About us

At Hiscox we care about our people. We hire the best people for the job and we’re committed to diversity and creating a truly inclusive culture, which we believe drives success. 

As an international specialist insurer we are far removed from the world of mass insurance products, selectively focusing on key areas of expertise and strength, all of which is underpinned by a culture that encourages us to challenge convention and always look for a better way.

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