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Quantitative Analytics Specialist 2 – Model Validation

Wells Fargo

This is a Full-time position in New York, NY posted May 20, 2021.

Job Description

Important Note: During the application process, ensure your contact information (email and phone number) is up to date and upload your current resume when submitting your application for consideration. To participate in some selection activities you will need to respond to an invitation. The invitation can be sent by both email and text message.  In order to receive text message invitations, your profile must include a mobile phone number designated as ‘Personal Cell’ or ‘Cellular’ in the contact information of your application. 

At Wells Fargo, we are looking for talented people who will put our customers at the center of everything we do. We are seeking candidates who embrace diversity, equity and inclusion in a workplace where everyone feels valued and inspired.

Help us build a better Wells Fargo. It all begins with outstanding talent. It all begins with you.

As the company’s second line of defense, Corporate Risk — or Independent Risk Management — provides independent oversight of risk-taking activities. Independent Risk Management establishes and maintains Wells Fargo’s risk management program and provides oversight, including challenges to and independent assessment of, the frontline’s execution of its risk management responsibilities. We manage risk according to the Risk Management Framework and ensure all employees understand their individual accountability for managing risk.

The Corporate Model Risk Group (CMoR) is seeking a quantitative analyst to join its Model Validation team.  Our diverse lines of business offer a world of opportunity to expand your capabilities and advance your career. We invest in our people and provide a supportive environment in which to learn and grow.
CMoR serves as the second line of defense to ensure the integrity of Wells Fargo’s model inventory.  This position will focus primarily on validations of models built in house and by third parties with various modeling technologies, including machine learning and AI technologies.  A validation project typically begins and ends with the analyst, requiring broad and continuous attention to detail, comprehensive documentation, and interactions with developers.  Meetings with auditors and regulators will require recall of validation details.   We use state-of-the-art mathematical, statistical, and machine/deep learning techniques to develop and assess models in these areas. We also use AI techniques (natural language processing, deep learning algorithms, and others) to model information in unstructured data (text, voice and images).

CMoR operates in a fast-paced work environment with continuously changing policies and technologies.  The successful candidate is expected to be self-motivated, require minimal supervision, and produce work that is consistent with CMoR’s recognized high standards.  Effective work will involve familiarity with source systems of record, analytical data and sampling plans, model replications, model performance assessments, and test model development as effective challenges to lines of business.  It further requires strength in writing detailed standard analytical reports to ensure Wells Fargo’s compliance with governance policies and regulations. 

Required Qualifications

  • A PhD in statistics, mathematics, physics, engineering, computer science, economics, or quantitative field; or a Master s degree in the above areas with 2+ years of experience in one or a combination of the previously mentioned fields above

Other Desired Qualifications

  • 2+ years of practical quantitative programming experience with SAS, SQL, Python, C++, R and comfortable working with large datasets 
  • Skill in managing relationships with key model stakeholders
  • Good team player and always willing to help and share
  • Ability to communicate to different audiences (other technical staff, senior management and regulators) both verbally and in writing
  • Aptitude for synthesizing data to ‘form a story’ and align information to contrast/compare to industry perspective
  • Attention to detail in both analytics and documentation
  • Eagerness to contribute collaboratively on projects and discussions and support the development of high performing teams
  • Capability to multi-task and finish work within strict timelines and provide timely requests for information and follow-up questions
  • Ability to work independently on complex model validations from start to finish
  • Perpetual interest in learning something new, but being comfortable with not knowing the all the answers
  • Intellectually curious.  Someone who enjoys solving problems
  • Knowledge of supervised and unsupervised machine learning methodologies such as GBM, XGBoost, and natural language processing (NLP)
  • Knowledge of software design patterns, software development life-cycle (SDLC) and Machine Learning Operations (MLOps) concepts
  • Analyzing processes and work flows to make recommendations for process improvement in various risk management and or business areas as well as participating in and leading model risk projects  

Street Address

NC-Charlotte: 401 S Tryon St – Charlotte, NC
NY-New York: 150 E 42nd St – New York, NY
IA-Des Moines: 800 Walnut St – Des Moines, IA
MN-Minneapolis: 600 S 4th St – Minneapolis, MN

Disclaimer

All offers for employment with Wells Fargo are contingent upon the candidate having successfully completed a criminal background check. Wells Fargo will consider qualified candidates with criminal histories in a manner consistent with the requirements of applicable local, state and Federal law, including Section 19 of the Federal Deposit Insurance Act.



Relevant military experience is considered for veterans and transitioning service men and women.

Wells Fargo is an Affirmative Action and Equal Opportunity Employer, Minority/Female/Disabled/Veteran/Gender Identity/Sexual Orientation.

Benefits Summary

Benefits
 

Visit https://www.wellsfargo.com/about/careers/benefits for benefits information.

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