Machine Learning Engineer | Python | FinTech | London Hybrid Working | £70,000-£90,000 + bonus + benefits
Method Resourcing are proud to be working exclusively with a financial services organisation who specialise in both asset and wealth management whose goal is to provide the customer with honest and reliable advice on their finances. This is an opportunity to join an established business who operate at the highest standard and work with some of the most exciting brands in finance.
The role:
Under the guidance of the Data Science Lead, you will collaborate with business and technology teams across WME to deliver machine learning models and actionable insights. Your primary responsibilities will include developing and managing MLOps pipelines along with the related infrastructure. You'll need experience with: Proven experience in deploying machine learning models into production environments. Extensive experience collaborating within cross-functional teams on data products and partnering with business stakeholders to support departmental or multi-departmental data initiatives. Advanced qualifications (MSc or PhD) or equivalent professional experience in computer science, statistics, applied mathematics, data management, information systems, information science, machine learning, or related quantitative fields. Proficient in agile methodologies with the ability to apply DevOps and MLOps practices to enhance communication, integration, code reuse, and automation across an organisation. Financial services background would be desirable. This is truly a fantastic opportunity for any ambitious engineer who's looking to join a high performing team within an exciting financial services institution. My client can offer the following benefits: Discretionary Bonus 25 days holiday plus bank holidays Pension scheme - employer 8% Income protection plan 70% of your salary Life assurance x4 of salary Private medical cover for you and your family & dental insurance Regular training and career development plans through coaching/mentoring and access to a learning portal Annual season ticket loans Employee discounts across a wide range of products Volunteering day paid for Wellbeing benefits including gym membership, employee assistance programme, eye care etc Working arrangements:
This role requires up to 3 days per week travel to London. Their office is located in central London so please ensure that you understand the working pattern before you apply. Please apply, and if you're interested in finding out more, please get in contact with Tom Morris by email (see below)
#J-18808-Ljbffr
Under the guidance of the Data Science Lead, you will collaborate with business and technology teams across WME to deliver machine learning models and actionable insights. Your primary responsibilities will include developing and managing MLOps pipelines along with the related infrastructure. You'll need experience with: Proven experience in deploying machine learning models into production environments. Extensive experience collaborating within cross-functional teams on data products and partnering with business stakeholders to support departmental or multi-departmental data initiatives. Advanced qualifications (MSc or PhD) or equivalent professional experience in computer science, statistics, applied mathematics, data management, information systems, information science, machine learning, or related quantitative fields. Proficient in agile methodologies with the ability to apply DevOps and MLOps practices to enhance communication, integration, code reuse, and automation across an organisation. Financial services background would be desirable. This is truly a fantastic opportunity for any ambitious engineer who's looking to join a high performing team within an exciting financial services institution. My client can offer the following benefits: Discretionary Bonus 25 days holiday plus bank holidays Pension scheme - employer 8% Income protection plan 70% of your salary Life assurance x4 of salary Private medical cover for you and your family & dental insurance Regular training and career development plans through coaching/mentoring and access to a learning portal Annual season ticket loans Employee discounts across a wide range of products Volunteering day paid for Wellbeing benefits including gym membership, employee assistance programme, eye care etc Working arrangements:
This role requires up to 3 days per week travel to London. Their office is located in central London so please ensure that you understand the working pattern before you apply. Please apply, and if you're interested in finding out more, please get in contact with Tom Morris by email (see below)
#J-18808-Ljbffr
