Responsibilities
* Design, train, and deploy machine learning models and data-driven tools to support investment and operational decision-making.
* Contribute to real production deployments of AI systems.
* Integrate ML models into business workflows, build data pipelines, and support rollout of AI applications across teams.
* Prototype ideas, test assumptions, and rapidly evolve solutions based on real user feedback and real-world constraints.
* Translate technical findings into clear, structured insights for collaborators across technical and business teams.
* Develop skills across the full ML lifecycle including data processing, modelling, evaluation, deployment, and ongoing improvement.
* Learn modern tooling and practices such as ML frameworks, cloud infrastructure, and MLOps tools for scalable AI systems.
Sample Projects You Might Work On
* GenAI for due diligence - support configuration, extension, and rollout of an in-house GenAI platform across investment teams; customise workflows, analyse model outputs, and drive adoption.
* Automated Deal Sourcing Tools - build prototypes that extract signals from datasets and integrate with APIs to enrich leads; support creation of modular ML-driven components usable across investment strategies.
Both examples are reframed so junior team members contribute meaningfully but are not expected to independently lead full workstreams.
Qualifications
* PhD graduates in a STEM field with applied ML, optimisation, or computational experience.
* Bachelor's/Master's graduates with 1-2 years of industry experience or relevant internships in machine learning, data engineering, or software engineering.
You will contribute to designing, implementing, and deploying production-grade ML systems ranging from NLP pipelines to model-driven workflow automation. You will learn quickly, gain real ownership, and see your work make tangible business impact. We don't expect candidates to have experience across all areas - what matters most is strong technical fundamentals, curiosity, and a willingness to learn quickly.
Foundational Skills
* Degree in a STEM field.
* PhD candidates: applied research involving ML, optimisation, simulation, statistics, numerical methods, NLP, or related areas.
* Bachelor's/Master's: 1-2 years of industry experience or relevant internships in ML, software engineering, or data engineering.
Programming Experience (especially Python)
* Experience writing clean, maintainable Python code.
* Applied AI experience such as exposure to LLM APIs (OpenAI, Azure OpenAI, Anthropic, etc.) and experience with small personal or internship projects building agents or AI-driven workflows.
* Agentic frameworks in Python is a plus but not required.
Data and Analytical Skills
* Comfortable working with data, performing analysis, and writing SQL queries.
* Experience building simple data pipelines or transformation workflows is a plus.
Exposure to ML Ops or Production Systems (Nice to Have)
* Familiarity with tools like MLflow, Weights & Biases, or cloud platforms (Azure, AWS, or GCP).
* Experience deploying models via APIs or lightweight services is a bonus, not a requirement.
Software Engineering Basics
* Understanding of Git/GitHub/Azure DevOps, testing basics, and general good engineering practices.
Mindset
* Strong problem-solving skills.
* Curiosity and eagerness to learn.
* Pragmatic, impact-driven approach.
* Ability to work collaboratively in a fast-paced environment.
About the Team
We are a growing team of AI specialists - data scientists, ML engineers, software engineers, and technology strategists - working to transform how a global investment firm with 65B+ in assets uses data and AI. We operate like a startup within the firm: fast, collaborative, and focused on delivering real value. Our work spans investment desks, portfolio companies, and core operations, giving early-career engineers wide exposure and the opportunity to grow rapidly.
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