Job Description
About the job
An investment firm is looking for a VP, Data Scientist to join their team.
Your new role
As Data Scientist, you will be responsible for:
- Build enterprise AI solutions using LLMs and advanced machine learning techniques
- Design and develop AI workflows to solve complex business challenges
- Deploy, optimize, and enhance AI applications based on user feedback
- Conduct research to drive innovation in investment and corporate functions
- Collaborate across departments to deliver impactful AI products and platforms
What you'll need to succeed
- Strong quantitative background in Computer Science, Engineering, Mathematics, Finance, or related fields
- Proven industry experience building and deploying LLM-powered AI solutions
- Strong software engineering foundation with coding best practices and scalable design principles
- Solid statistical and data science expertise, including hypothesis testing and model development
- Experience managing investment-related data and supporting end-to-end research initiatives
- 6+ years of hands-on ML/data science engineering experience with strong communication and stakeholder engagement skills
What you'll get in return
In return, you'll be part of an organization that values its employees. You'll be rewarded with:
- Structured career growth and plenty of developmental opportunities
- Hybrid and stable working environment
What you need to do now
If you're interested in this role, click apply now or for more information and a confidential discussion on this role or to find out about more opportunities in Technology contact Yuki Cheung or email [Confidential Information]. Referrals are welcome.
At Hays, we value diversity and are passionate about placing people in a role where they can flourish and succeed. We actively encourage people from diverse background to apply.
EA Reg Number: R22110258 | EA License Number: 07C3924 | Company Registration No: 200609504D
More Info
Key Skills
AI workflows
LLMs
scalable design principles
AI applications
machine learning techniques
data science expertise
coding best practices



