Overview: Macro Hive is a leading independent providerof global macro and financial market research. Our team ofexperienced researchers leverage quantitative techniques andcutting-edge technologies to develop innovative and data-drivensolutions to complex financial problems, helping our clients makeinformed investment decisions and stay ahead of the competition. Weare seeking talented, motivated interns with solid technical skillsto work with us in our Quantitative Research team focusing onapplications of AI to finance. This will include researching alphasignals and building state-of-the-art machine learning modelsacross various asset classes. You should be in your final year ofstudies in a quantitative field from a Russel group university orequivalent. Proficiency with Python programming is essential,alongside expertise in applications of machine learning (ML), deeplearning (DL), or natural language processing (NLP) – we use allthe latest technologies including LLMs and the wider GenAI techstack. Responsibilities: · Research: working alongside researcherson end-to-end research projects, including on data analysis, alphageneration, trading models, and applications of LLMs/GenAI tofinance. · Development: building and enhancing tools for the quantand data workflow. · Data: sourcing new alternative data sets forthe quant and data workflow. This will include: · Conductingresearch and analysis on financial data sets using advancedmodelling and machine learning techniques. · Helping implement andimprove existing models and algorithms. · Helping prepare anddeliver research reports to clients. · Staying up to date with thelatest developments in AI, time series analysis, and quant finance.Qualifications: Required: · Education: BSc/MSc/PhD in a technicaldegree, including but not limited to Mathematics, QuantitativeFinance, Physics, Computer Science, or Engineering. · MachineLearning: Experience working with machine learning techniques(Decision Trees, Random Forests, XGBoost, etc.) for supervisedregression and classification tasks. Knowledge of unsupervisedlearning, NLP (transformers, LLMs etc.), deep learning frameworks(TensorFlow, PyTorch etc.), and architectures for sequential data(RNN, LSTM etc.) is a plus. · Statistical Analysis: you should havea good foundation in statistics and be comfortable with things liketime series analysis, hypothesis testing and regression analysisetc. · Python: You should be proficient in Python programming usingthe ML/scientific stack: NumPy, Pandas, scikit-learn etc. · ProblemSolving: Ability to clearly convey data-driven ideas for complexproblems and translate them to clean, robust, and efficient code.Desirable: - Experience with object-oriented Python. - Experiencewith web-scraping. - Experience with cloud services (Azurepreferred). - Experience with DevOps tools (Git, Docker etc.) -Experience working with financial data or tradingmodels.