Are you deeply curious financial data scientist looking to lead analytical strategies to tie analytics to business results? This role may be an exciting opportunity for you.

As a Data Scientist Associate Senior within our Consumer and Investment Banking Financial Analytics team, you will  collaborate with cross-functional teams to drive rigorous quantitative analyses that translate financial insights into actionable plans. You will have the opportunity to apply your finance expertise and data science techniques, including AI, ML, and advanced econometric methods, to make a tangible impact on business results.

Job Responsibilities:

Develop and deploy machine learning models and generative AI capabilities.Design, code, test, and debug applications.Collaborate with cross-functional teams to achieve common goals.Keep stakeholders informed on development progress and benefits.Manage project lifecycle and software development deliverables.Solve complex problems and handle ambiguity with strong analytical skills.Develop insights, methods, or tools using various analytic methods such as causal-model approaches, predictive modeling, regressions, machine learning, time series analysis, etc.Handle large amounts of data from multiple and disparate sources, employing advanced Python and SQL techniques to ensure efficiency and accuracy.Uphold the highest standards of data integrity and security, aligning with both internal and external regulatory requirements and compliance protocols.

Required qualifications, capabilities, and skills :

Bachelors or Masters Degree in Finance, Quantitative Finance, Data Science, Economics, or a related field.Proficient programming skills in python and knowledge of software engineering best practicesStrong knowledge of basic data science libraries in Python (NumPy, pandas, scikit-learn, pyspark)Understanding of the main deep-learning frameworks such as PyTorch, TensorFlow, KerasExperience with Linux and shell scripting and experience with LaTeXSolid understanding of traditional data science techniques and experience with data engineer pipelines for big dataSolid knowledge of RNNs, and LSTMs models.

Preferred qualifications, capabilities, and skills:

Experience in big data platforms such as Databricks, AWS EMR, Sagemaker, Apache Glue, Spark, etc.Experience/understanding of cloud storage (Object Stores like S3, Blob; NoSQL like Columnar, Graph databases).