Senior Manager, Data Science

1 week ago

Jersey City, New Jersey, United States Fidelity TalentSource LLC Full time
Job Description:

We are seeking a highly skilled Senior Manager, Data Science to join our team at Fidelity Investments. As a key member of our data science team, you will be responsible for leading large-scale data preprocessing, manipulation, and analytical tasks. You will develop supervised and unsupervised Machine Learning (ML) algorithms, advanced feature selection and reduction techniques, clustering, and hyperparameter optimization. You will also research and build sophisticated, innovative, and scalable Artificial Intelligence (AI) algorithms, models, and platforms to improve customer experience and drive business outcomes.

Primary Responsibilities:
  • Conduct data ingestion and cleaning, preparing substantial data volumes for detailed analysis using advanced data wrangling and preprocessing methodologies.
  • Design analytical solutions for abstract business challenges, employing data mining, ML, and Deep Learning (DL) techniques to devise predictive and prescriptive models.
  • Research and develop state-of-the-art AI algorithms and methods to tackle complex and high-impact business issues.
  • Evaluate and validate ML models to ensure their accuracy, reliability, and generalizability.
  • Implement and monitor NL models in production environments, ensuring their optimal performance and scalability.
  • Collaborate with business stakeholders to identify, define, and sequence data science initiatives, converting top analytical priorities into comprehensive business and technical specifications.
  • Communicate and present data science results and insights to both technical and business stakeholders ensuring clarity and actionable understanding.
Requirements:

We are looking for a candidate with a 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.

Alternatively, a 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 in 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).
  • Demonstrated expertise in 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.
  • Demonstrated expertise in 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.
  • Demonstrated expertise in 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.

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