Our client a top-tier Management, Strategy and IT Consulting firm is seeking to bring on a Data Scientist onto their Wholesale and Retail Banking practice to support various consulting projects/efforts with their Financial Services Banking end clients.

The Global Markets Data Analytics team based in New York and Raleigh have a mission to use big, alternative and directly sourced data to describe and predict key metrics of companies and economies so that we may deliver innovative data solutions and insights to our internal analyst teams and the firm's clients. We are looking for self-starting and innovative senior data scientist to join and grow our team. The perfect candidate will have a background in a quantitative or technical field, will have experience building AI ML models and pipelines on large datasets, and will have experience in evidence based insight generation. You are focused on innovation, insight delivery, problem solving, and have demonstrated success in delivering AI ML based products. You must have a keen interest in financial markets and experience with modern analytical tools and methods, including processing data at large scale for analytical purposes, while maintaining proper empirical discipline and rigor.

Responsibilities:

• Use machine learning and financial quantitative modeling to generate insights for alternative data sets like geo special, text etc.
• Design and build model execution pipeline with model validation, model monitoring, model scoring, model decay and retraining
• Work with street leading analysts to facilitate their requests for new and innovative data analytics.
• Help design and construct the data processing and analytics infrastructure necessary to perform advanced analytical research within a global enterprise.
• Create original insights from data by executing applied empirical research in a timely manner

Qualifications:

• A PhD, or Masters with equivalent work experience, with a solid background in machine learning, statistical analytical techniques, quantitative social science research or experimental physical science.
• Knowledge of financial modelling, factor investing, risk modelling (CFA / FRM certification is a plus)
• Advanced Knowledge of Python, Scala and Java
• 3+ years of profession experience and discipline in building Machine Learning models
• 3+ years of experience in Statistics and Data Science techniques like exploratory analysis, feature engineering and ML techniques like clustering, regressions, classifications etc.
• Experience with machine learning packages such as zipline, pyfolio, fbprophet, pysf, pyFlux, pyramid, TensorFlow, PyTorch, Keras, Scikit-Learn, NumPy, SciPy, Pandas, StatsModels, Spark ML
• Experience in Machine Learning techniques like hyper parameter tuning, model validation, model serving, model monitoring, retraining etc. (Machine Learning pipeline)
• Experience with machine learning lifecycle tools (i.e. mlflow, kubeflow)
• Excellent written and spoken English, with ability to work collaboratively and communicate well within a highly skilled team, with a wide range of backgrounds and skillsets.
• Innovative mindset—thinks beyond the status quo