Machine Learning Researcher Job Description Template

The machine learning researcher applies modern statistical learning to financial prediction problems, where the signal to noise ratio is far lower and the data far less stationary than in most machine learning domains. The role demands both genuine method knowledge and the understanding that a model which performs beautifully in cross validation may be reading the future. Careful validation design is most of the work.

Typical Duties and Responsibilities

• Apply machine learning methods to prediction problems in financial data
• Design validation schemes appropriate to non stationary time series
• Engineer features from market, fundamental and alternative data
• Compare model families and justify the choice on evidence
• Quantify uncertainty and the stability of model output
• Work with quantitative developers to productionise models
• Monitor deployed model performance and detect drift
• Investigate why models degrade and whether retraining helps
• Document methodology, data lineage and validation results
• Present findings to researchers, portfolio managers and risk

Education

• PhD or Master’s in machine learning, statistics, computer science or a related field

Required Skills and Experience

• 3+ years applying machine learning to real prediction problems
• Deep understanding of validation, regularisation and overfitting
• Proficiency with Python and a modern machine learning framework
• Experience with time series data and its particular pitfalls
• Ability to design experiments that would detect a false positive
• Understanding of feature engineering and leakage
• Clear communication of model behavior and its limits
• Comfort with the low signal to noise ratio of financial data

Preferred Qualifications

• Prior finance experience
• Publication record or strong competition results