Applied AI/ML Associate Senior - Causal ML

Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team. Our team focuses on applying GenAI, ML and statistical models to solve business problems in the Global Wealth Management space.

As an Applied AI/ML Senior Associate within our dynamic team in Asset and wealth Management , you will apply your quantitative, data science, and analytical skills to complex problems. We are seeking a Data Scientist with strong foundations in causal inference, machine learning, statistical modeling, and applied experimentation to help build next-generation decision systems across pricing, campaign targeting, and related business use cases. This role is ideal for someone who can move beyond prediction and help the organization understand cause-and-effect relationships in real-world, observational settings.

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