About the Role

The Senior Quantitative Researcher will have the opportunity to try to „crack“ the financial markets, learn a lot, and have some fun while at it.  They will be working with top-quality financial and alternative data, cutting-edge infrastructure, and vast computational resources to find a systematic and sustainable advantage in the markets. The role will involve partnering and growing together with experienced investment professionals, data scientists, and software developers in a team-based environment in the office. The work will be both intellectually challenging and satisfying and the researcher will be rewarded proportionately to the out-of-sample performance of their models. PharVision is committed to providing flexible career development and long-term growth opportunities.

Beyond that, the PharVision team values self-awareness, intellectual curiosity, and a team-player mentality. You will be working side-by-side and learning from experienced investment professionals and quantitative researchers.

Responsibilities

• Research, design, develop and deploy advanced predictive machine learning models (hands-on)
• Develop and implement systematic trading strategies, based on the model predictions
• Read financial literature and academic papers in search of market inefficiencies and inspiration
• Form hypotheses in relation to market patterns and dependencies and test them rigorously
• Learn about financial markets and instruments and find ways to predict their future performance
• Think creatively, innovate and solve complex problems

Technical Requirements

• At least a graduate degree in a technical field
• Eagerness to learn about financial markets and ways to predict performance of businesses and their stock prices
• 5+ years of experience analyzing large amounts of data using Python or R
• Extensive experience training and implementing models with Scikit-learn, Pytorch and/or Tensorflow
• Understanding of linear algebra, time series analysis, data mining, numerical methods, and statistical tools, including PCA and regression
• A track record of demonstrated dedication and perseverance in solving complex problems
• Excellent English, both spoken and written

Soft skills requirements

• Excellent communication skills, both written and oral
• Self-awareness, intellectual curiosity, and a team-player mentality
• Self-starter, enthusiastic and positive thinker

Nice to Have

• Degree(s) in a technical or quantitative discipline, like statistics, mathematics, physics, electrical engineering, or computer science
• Exposure to packages such as pandas, NumPy, statsmodels, sklearn, scipy, matplotlib, and TensorFlow. C++ experience is an advantage
• Exposure to the Agile approach and methodology
• Experience or at least interest in machine learning techniques, time series analysis, and econometrics
• Experience in Deep Learning: DNN, CNN, RNN/LSTM, GAN, or other autoencoders
• KX / KDB+ / q experience
• Basic understanding of equity markets