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Wells Fargo Lead Quantitative Analytics Specialist 
India, Karnataka, Bengaluru 
193408868

18.08.2024


In this role, you will:

  • Lead complex initiatives including creation, implementation, documentation, validation, articulation, and defense of highly
    statistical theory
  • Qualify monitor markets and forecast credit and operational risks
  • Strategize short and long-term objectives, and provide analytical support for a wide array of business initiatives
  • Utilize stochastic, structured securities, spread analysis, with the expertise in the theory and mathematics behind the analysis
  • Review and assess models inclusive of technical, audit, and market perspectives
  • Identify structure and scope of review
    Enable decision making for product and marketing with broad impact and act as key participant to develop and document
    analytical models
  • Collaborate and consult with regulators and auditors
  • Present results of analysis and strategies
  • Work with the technology team to deploy appropriately detailed solution


Required Qualifications:

  • 8-10 years of experience as a Python Developer on strong projects.
  • Bachelor's or Master’s degree in Computer Science, Software Engineering, data science or quantitative field.
  • In-depth understanding of the Python software development stacks, ecosystems, frameworks, and tools such as Numpy, Scipy, Pandas, Dask, spaCy, NLTK, sci-kit-learn and PyTorch.
  • Experience with popular Python frameworks such as Django, Flask or others.
  • Good database skills including SQL.
  • Excellent problem-solving ability with solid communication and collaboration skills.
  • Self-starter, takes initiative, and has a strong desire to learn and grow.
  • 5+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following:
    work experience, training, military experience, education
  • Master's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science


Desired Qualifications:

  • Knowledge of data science and machine learning concepts and tools.
  • 8-10 years of experience as a Python Developer on strong projects.
  • Bachelor's or Master’s degree in Computer Science, Software Engineering, data science or quantitative field.
  • In-depth understanding of the Python software development stacks, ecosystems, frameworks, and tools such as Numpy, Scipy, Pandas, Dask, spaCy, NLTK, sci-kit-learn and PyTorch.
  • Experience with popular Python frameworks such as Django, Flask or others.
  • Good database skills including SQL.
  • Excellent problem-solving ability with solid communication and collaboration skills.
  • Self-starter, takes initiative, and has a strong desire to learn and grow.
  • Familiarity with Natural Language Processing concepts and tools.
  • Familiarity with Generative Artificial Intelligence concepts.
  • Experience in financial industries including risk management will be a plus
  • Strong project management skills and ability to coordinate several reports / projects at once
  • Excellent verbal, written, and interpersonal communication skills
  • Experience preferred from a large, matrixed, global financial services company
  • Bachelors/Master’s degree preferably in an engineering or quantitative discipline
  • Establish and maintain a roadmap for AI and machine learning initiatives, ensuring alignment with business goals and timelines.
  • Develop and deploy machine learning models and algorithms to solve business problems in the financial risk management.
  • Analyze large, complex datasets to extract actionable insights and drive data-driven decisions.
  • Work closely with stakeholders to understand business requirements and translate them into technical solutions.
  • Collaborate with software developers to integrate AI / ML solutions.
  • Participate in regular meetings with and provide AI / ML related updates to relevant senior management.

Wells Fargo Recruitment and Hiring Requirements:

b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.