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