Wells Fargo Lead Quantitative Analytics Specialist 

India, Karnataka, Bengaluru 

578026623

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

Required Qualifications:

• 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:

• Required to work individually or as part of a team on multiple data science projects and work closely with business partners across the organization. Mentor and coach budding Data Scientist on developing and implementing data science solutions.
• Perform various complex activities related to statistical/machine learning. Provide analytical support for developing, evaluating, implementing, monitoring and executing models across business verticals using emerging technologies including but not limited to Python, Spark, and H2O etc.
• Expert knowledge on working on large datasets using SQL and present conclusions to key stakeholders.
• Establish a consistent and collaborative framework with the business and act as a primary point of contact in delivering the solutions.
• Experience in building quick prototypes to check feasibility and value to business.
• Expert in developing and maintaining modular code-base for reusability
• Review and validate models and help improve the performance of the model under the preview of the banking regulations.
• Work closely with multiple teams such as technology, ML Ops, MRM teams to deploy the models to production.
• Prepare detailed documentations for projects for both internal and external that complies regulatory and internal audit requirements
• Mentor and guide junior team members and work on multiple analytics/data science initiatives.
• PhD in statistics/ economics/ mathematics/ operations research or similar is mandatory
• 3+ years of must have hands on exposure in Python, PySpark and SQL.
• Expert knowledge of libraries like sckit-learn, pandas, numpy, mllib, matplotlib, keras
• Expert in data mining and statistical analysis.
• 5+ Experience in developing, implementing models.
• Statistical models – linear regression, logistic regression, time series analysis, multivariate statistical analysis
• Machine learning models – Random Forest, XGBoost, GBM, SVM
• Exposure to deep learning framework - ANN,RNN, CNN, LSTM
• Excellent understanding of model metrics including AUC, ROC, F-statistics etc. with clear understanding of how model performance is tuned
• Strong programing skills.
• Hands on knowledge in one or more of Big Data skills – SQL, Aster, Teradata, Hadoop, SPARK, H20, Big Query.
• Exposure to Google Cloud Platform
• Critical thinking and strong problem solving skills
• Ability to learn the business aspects quickly and handle multiple projects.

Job Expectations:

• 2+ years of knowledge of banking industry and products in at least one of the LOB such as credit cards, mortgage, deposits, loans or wealth management etc.is desirable
• Knowledge of functional area such as risk, marketing, operations or supply chain in banking industry is desirable
• Ability to multi-task and prioritize between projects
• Ability to work independently and as part of a team
• Working expertise in Tensorflow, Keras or Pytorch would be added advantage.
• Working knowledge in developing end-to-end AI/ML pipeline in Apache Spark, Sparkling Waters, H2O, Caffe inHortonworks/Cloudera/MapRor Teradata Aster big data platforms.
• Ability to work with Data Engineers in discovering and optimizing bottlenecks in the AI/ML pipeline for real-time or near-real-time applications that consumes large throughput of data

Wells Fargo Recruitment and Hiring Requirements:

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