Senior Machine Learning Engineer (Finance)

• Dive deep into research, design, code, test, and deploy machine learning models that directly impact portfolio construction and market forecasting.
• Take full ownership of exciting projects, from initial concept to seamless production, ensuring top-tier quality, scalability, and maintainability.
• Employ state-of-the-art techniques to crack intricate problems in financial analytics and push the boundaries of what's possible.
• Stay at the forefront of technology by continuously researching and implementing new methodologies to boost model accuracy and efficiency.
• Collaborate closely with internal stakeholders, translating business needs into powerful technical solutions and managing expectations along the way.
• Clearly articulate complex technical concepts to non-technical audiences, ensuring everyone is aligned with our ambitious business objectives.
• Guarantee all solutions comply with stringent data security, governance, and regulatory standards that are paramount in the financial sector.
• Implement rigorous testing and monitoring frameworks to safeguard system integrity and maintain peak performance.

• You possess an advanced degree, ideally a Bachelor’s, Master’s or Ph.D. in Computer Science, Data Science, Machine Learning, Financial Engineering, or a related field.
• You've cultivated at least 5 years of hands-on experience in AI, machine learning, or data science, and you've demonstrably built and deployed models that perform in real-world production environments.
• You've gained valuable exposure to the financial sector, particularly in areas like time series forecasting, portfolio optimization, and quantitative analysis.
• You're highly proficient in coding with Python, allowing you to efficiently develop and implement complex algorithms and models.
• You've extensively worked with leading machine learning frameworks such as PyTorch and scikit-learn, enabling you to build sophisticated and effective solutions.
• You possess a solid background in data architecture, big data technologies, and cloud platforms like AWS, GCP, or Azure.
• You've acquired a working knowledge of financial analytics, risk modeling, and quantitative finance methodologies.
• You've explored the potential of large language models (LLMs) and frameworks like LangChain and LlamaIndex.
• You've applied convex optimization techniques to refine portfolio management strategies.
• You're familiar with financial econometrics or related quantitative finance methods.

• A collaborative and inclusive corporate culture that values innovation and initiative
• Opportunities for professional growth and career progression
• Flexible working options

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