Senior Manager, Data Science

Key Skills

Aartificial intelligence, Amazon Simple Storage Service (S3), AWS, Data science techniques, Deep Learning, Effective communication skills, Machine learning techniques, Pandas, Python Programming, Scikit-learn, SQL Server, XGBoost

Job Description

Performs large-scale data preprocessing, manipulation, and analytical tasks. Develops supervised and un-supervised Machine Learning (ML) algorithms (regression, decision trees/random forest, neural networks), advanced feature selection and reduction techniques, clustering, and hyperparameter optimization. Researches and builds sophisticated, innovative, and scalable Artificial Intelligence (AI) algorithms, models, and platforms to improve customer experience and drive business outcomes. Leads initiatives involving extensive multi-dimensional databases, complex business infrastructure, and collaboration with cross-functional teams.

Primary Responsibilities:

• Conducts data ingestion and cleaning, preparing substantial data volumes for detailed analysis using advanced data wrangling and preprocessing methodologies.
• Designs analytical solutions for abstract business challenges, employing data mining, MLg, and Deep Learning (DL) techniques to devise predictive and prescriptive models.
• Researches and develops state-of-the-art AI algorithms and methods to tackle complex and high-impact business issues.
• Evaluates and validates ML models to ensure their accuracy, reliability, and generalizability.
• Implements and monitors NL models in production environments, ensuring their optimal performance and scalability.
• Collaborates with business stakeholders to identify, define, and sequence data science initiatives, converting top analytical priorities into comprehensive business and technical specifications.
• Communicates and presents data science results and insights to both technical and business stakeholders ensuring clarity and actionable understanding.

Education and Experience:

Bachelor’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, Computational Finance, or a closely related field and five (5) years of experience as a Senior Manager, Data Science (or closely related occupation) performing data analytics, building statistical models, and developing business intelligence applications to improve customer experience and drive business results in a financial services environment.

Or, alternatively, Master’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, Computational Finance, or a closely related field and three (3) years of experience as a Senior Manager, Data Science (or closely related occupation) performing data analytics, building statistical models, and developing business intelligence applications to improve customer experience and drive business results in a financial services environment.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) performing large-scale data extraction, transformation, and loading processes in large data warehouses with SQL, Apache Hive, or Python; and integrating and managing data across databases and platforms (SQL Server, Oracle, or AWS S3).
• DE designing and building Machine Learning (ML) models (regression, classification, decision trees, clustering, and recommender systems) and Deep Learning (DL) models (LSTM, VAE, and Transformers) in Python using Scikit-Learn, Transformers, or PyTorch; and performing distributed model training and hyperparameter optimization using Amazon Web Services (AWS) SageMaker.
• DE validating/assessing model soundness and performance by conducting in-sample and out-of-sample testing using RMSE, R-squared, AUC ROC, Precision, or Recall; and documenting model assumptions, methodologies, limitations, and usage guideline to ensure adherence to model governance and model risk management policies.
• DE designing and executing analytical solutions to enhance the efficiency of financial operations and services by automating financial data and document processing to reduce manual intervention; identifying financial fraud/anomalies through employing advanced data analytics using Python with Scikit-Learn, Pandas, or Statsmodels; and estimating and measuring customer propensities to pursuing financial products and services using Python with Scikit-Learn, XGBoost, or LightGBM.

Salary: $182,500.00 – $192,500.00/year.