ROLE AND RESPONSIBILITIES 

As a Quantitative Developer at Opto, you will be responsible for developing and implementing sophisticated algorithms for our robo-advisory services. You will play a key role in building and refining models that support portfolio management, stock scoring, and financial analytics. Your primary focus will be on: 

• Algorithm Development: Design and develop quantitative models for robo-advisory services, incorporating risk management, asset allocation, and portfolio optimisation strategies. 

• Stock Scoring Models: Create advanced scoring models to analyse stock performance, utilizing large datasets and machine learning techniques. 

• Integration of LLMs: Explore and integrate Large Language Models (LLMs) where applicable to enhance financial predictions and analysis. 

• Data Management: Handle large-scale financial data for algorithm development, including data cleansing, feature extraction, and model validation. 

• Back testing and Validation: Perform back testing of strategies to ensure robustness and reliability in real-world trading environments. 

• Collaboration: Work closely with data scientists, portfolio managers, and software engineers to deploy models in production. 

• Continuous Learning: Stay updated with the latest advancements in financial engineering, machine learning, and artificial intelligence to ensure our models remain competitive. 

• Documentation and Reporting: Prepare detailed documentation of models, methodologies, and performance metrics, ensuring transparency and auditability. 

KEY SKILLS AND EXPERIENCE 

• Experience: 5+ years of experience in quantitative development, preferably within the financial services industry. 

• Technical Expertise: Strong proficiency in Python, with experience in data analysis libraries like Pandas, NumPy, and machine learning frameworks such as TensorFlow or PyTorch. 

• Mathematical Rigor: In-depth knowledge of quantitative finance, including stochastic calculus, optimization, and statistical modelling. 

• AI and LLMs: Experience with natural language processing (NLP) and working knowledge of Large Language Models such as GPT to enhance financial data analysis. 

• Financial Market Knowledge: A strong understanding of equity markets, derivatives, and portfolio theory. 

• Analytical Skills: Excellent problem-solving skills with the ability to design algorithms that are efficient and scalable. 

• Communication: Strong written and verbal communication skills to convey complex quantitative concepts to non-technical stakeholders. 

• Attention to Detail: A high level of attention to detail and a commitment to maintaining the accuracy and reliability of the models.