At a Glance
- Tasks: Design and implement ML solutions to enhance investment processes and client experiences.
- Company: Morgan Asset Management, a leader in innovative data science.
- Benefits: Competitive salary, flexible working options, and continuous learning opportunities.
- Other info: Collaborative culture with a focus on hands-on problem solving and career growth.
- Why this job: Shape the future of data science while making a real impact in finance.
- Qualifications: Advanced degree or significant experience in quantitative fields; strong Python and ML skills.
The predicted salary is between 63000 - 77000 £ per year.
As an innovative data scientist in Morgan Asset Management's Data Science team, you will design and implement ML solutions to enhance investment processes, elevate client experiences, and streamline operations. Initially, you will be focused on developing solutions to support our ESG and Stewardship functions with a heavy focus on content extraction, search and principals-based reasoning with LLMs. Your technical expertise will drive impactful results, and you’ll play a key role in shaping our data science capabilities. You’ll thrive in a collaborative culture that values hands-on problem solving and continuous learning.
Job Responsibilities
- Collaborate with internal stakeholders to understand business needs, build out requirements, and design technical architectures.
- Develop technical solutions utilising LLMs with a focus on problems involving search, content extraction and principal-based reasoning.
- Build comprehensive evaluation packages to ensure the efficacy and reliability of solutions and to build trust with stakeholders.
- Help to design technical architectures and solutions.
- Collaborate heavily with engineering functions to deliver high quality, scalable output.
- Stay up to date with the latest developments in AI and become an SME within the data science function.
Required qualifications, capabilities, and skills
- Advanced degree (MS or PhD) in a quantitative or STEM discipline or significant practical experience in industry.
- Commercial experience in applying NLP, LLM and ML techniques in solving high-impact business problems, such as semantic search, information extraction, question answering, personalisation, classification, recommendation or forecasting.
- Advanced python programming skills with experience writing production quality code using ML libraries and deep learning frameworks.
- Good understanding of the foundational principles and practical implementations of ML algorithms such as clustering, decision trees, deep learning, reinforcement learning, etc.
- Strong knowledge of NLP, language modelling, prompt engineering, and domain adaptation.
- Ability to communicate complex concepts and results to both technical and business audiences.
Preferred qualifications, capabilities, and skills
- Strong analytical skills with an understanding of financial markets and asset management line of business.
- Strong business domain knowledge in ESG, investment stewardship, or buyside investment.
- Familiarity with techniques for model explainability and self-validation.
- CFA or equivalent financial qualification.
Data Scientist Lead in London employer: JP Morgan Chase
Morgan is an exceptional employer, offering a dynamic work culture that prioritises diversity and inclusion while fostering employee growth through comprehensive coaching and development opportunities. As a global leader in financial services, we empower our teams to drive impactful product management and AI enablement, ensuring that every employee can contribute meaningfully to our clients' success in a collaborative environment located at the heart of the financial sector.
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We think you need these skills to ace Data Scientist Lead in London
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Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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