Applied AI/ML Associate Senior - Causal ML

Job responsibilities

• 
  • Engage with stakeholders and understanding business requirements 
  • Develop AI/ML solutions to address impactful business needs 
  • Work with other team members to productionize end-to-end AI/ML solutions 
  • Engage in research and development of innovative relevant solutions 
  • Document developed AI/ML models to stakeholders 
  • Coach other AI/ML team members towards both personal and professional success 
  • Collaborate with other teams across the firm to attain the mission and vision of the team and the firm 
   

Required qualifications, capabilities, and skills

•  Strong quantitative training in Statistics, Data Science, Economics, Computer Science, Applied Mathematics, Operations Research, or a related field.

• Strong understanding of causal inference fundamentals, including confounding, mediation, selection bias, and identification assumptions.

• Practical knowledge of techniques used to control for confounding and estimate causal effects in observational data.

• Familiarity with causal reasoning concepts such as backdoor criterion, frontdoor criterion, and treatment effect estimation.

• Advanced degree in analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis, Operations Research)

• Experience in the application of AI/ML to a relevant field

• Demonstrated practical experience in machine learning techniques, supervised, unsupervised, and semi-supervised

• Strong experience in natural language processing (NLP) and its applications

• Solid coding level in Python programming language, with experience in leveraging available libraries, like Tensorflow, Keras, Pytorch, Scikit-learn, or others, to dedicated       projects

• Previous experience in working on Spark, Hive, and SQL 
   

Preferred qualifications, capabilities, and skills

• Industry experience applying causal machine learning to pricing, marketing, campaign targeting, personalization, or customer analytics.

• Experience with temporal causality, longitudinal data, panel data, or dynamic treatment effects.

• Experience with time series forecasting or combining causal inference with time-dependent modeling.

• Familiarity with experimentation, A/B testing, quasi-experimental design, or synthetic control methods.

• Experience with modern causal ML methods such as meta-learners, uplift models, causal forests, or double machine learning.

• Financial service background .PhD/Masters