ML Engineer

About the company:

Danske Bank is a Nordic bank with strong local roots and bridges to the rest of the world. For more than 145 years, they have helped people and businesses in the Nordics realise their ambitions. They want to help their customers become financially confident and help them build their lives and businesses on a solid financial foundation. They aim to create long-term value for all their stakeholders – their customers, shareholders and the societies they are part of – and their vision is to be recognised as the most trusted financial partner.

Assignment description:

Consultant will become a part of the ML Engineering CoE working in the Advanced Commercial Analytics squads. The focus will be to productionize the developed ML models and ensure continuously integration of the model code developed by the Data Scientists. The purpose of the commercial data science initiative is to assist the bank in driving sales and reducing churn. The project is seeking a contractor to speed up the deliveries for developed solutions in various business segments.

Requirements:

Contractors will be part of the Commercial Analytics squad working in multiple countries. The main responsibility is to

1. - Help deploy and operate prioritised models to PROD

2. - Educate CoE in DE and MLOps best practices and help draw up internal guidelines

3. - Assist in pipeline foundation development or tailoring to ACA CoE needs together with the infrastructure department:

a. Data pipelines

b. CI/CD

c. Scheduling

d. Monitoring

All this to ensure the Advanced Commercial Analytics department reaches it commercial goals in 2021.You main responsibility will be to build the pipelines for the statistical models utilizing the existing tech stack as well as bringing your experience into play to leverage new technologies. All tasks will be solved as part of multifunctional DevOps squads consisting of broad capabilities delivering end to end Data Science solutions. You will therefore be working closely with experienced Data Scientists and Machine Learning Operations Engineers as well as the business units who are consuming the data science services.