About this role:
Enterprise Analytics and Data Science (EADS) is seeking a Lead Quantitative Analytics Specialist within the Artificial Intelligence Machine Learning Center of Excellence.
Primarily this role will apply business knowledge and advanced programming skills and analytics to serve as a subject matter expert, analyst, advisor and consultant to the EADS Modeling COE supporting model lifecycle; model performance monitoring, performance testing, data analysis, model implementation, model production, delivery of model results, change management, documentation and governance.
In this role, you will:
- Need to work individually or as part of a team on data science projects and work closely with business partners across the organization.
- Perform various complex activities related to statistical/machine learning models. Provide analytical support for porductionalizing, evaluating, implementing, monitoring and executing models across business verticals using technologies including but not limited to Python, Spark, and H2O etc.
- Develop dynamic dashboards; analyze key risk parameters to help understand changes in business and model performance
- Identify opportunities and deliver process improvements, standardization, rationalization and automations. Enhance and standardize performance analysis, reporting packages
- Maintain documentation for implementation and monitoring processes across the team with focus on standardization of controls
- Provide thought and functional leadership to team and drive new solutions and improvement of existing ones
- Project and stakeholder management for self and team's deliverables
- BS degree or higher in a quantitative field such as applied math, statistics, physics, accounting, finance, economics, econometrics, or business/social and behavioral sciences with a quantitative emphasis
- Bachelors or Master’s degree in engineering field like computer science and engineering, Information technology, Electrical Engineering, Electronics and Telecommunication Engineering etc.
- 9+ years of relevant experience
- Experience in implementing, productionalizing, model monitoring of supervised, unsupervised and semi-supervised model techniques including but not limited to Random Forest, GBM, Ridge-Lasso-ElasticNet, XGboost etc. Time-series techniques like Arima (and the family), Arch, Garch etc.
- Experience implementing, productionalizing, model monitoring of machine learning algorithms such as support vector machines, decision trees, logistic regression, clustering, neural networks, graphical models etc.
- Excellent understanding of model metrics including AUC, ROC, CAP-curve, F-statistics etc. with clear understanding of how model performance is tuned
- Excellent hands-on on with Drift and Lift analysis and related model monitoring metrics
- Experience in implementing, model monitoring of Deep-learning, Artificial intelligence techniques like ANN, CNN, DNN, RNN etc. and how to strategize deep-learning layer and activations.
- Strong programing skills.
- Expertise in one or more analytic tools like : Python (with Anaconda), PySpark, H2O
- Experience in one or more of Big Data skills – SQL, Aster, Teradata, Hadoop, SPARK, H20 and various big data distributions like Hortonworks and MapR
- Model Monitoring for NLP, Text mining, Image/Voice processing, digital analytics, deep learning, machine learning models
- Proven experience in identifying threshold values for the related model monitoring metrics and suggestions being passed on to data scientist and business when model re-training required.
- Demonstrate excellent organization skills throughout the development of analytical solutions (data analysis documentation, hypothesis documentation, code management, etc.).
- Strong ability to develop partnerships and collaborate with other business and functional areas
- Experience determining root cause analysis
- Working expertise in Tensorflow, Keras or Pytorch would be added advantage
- Ability to translate analytical data into useful business information
- Critical thinking and strong problem solving skills
- Ability to learn the business aspects quickly
- Knowledge of banking industry and products in at least one of the LOB such as credit cards, mortgage, deposits, loans or wealth management etc.
- Knowledge of functional area such as risk, marketing, operations or supply chain in banking industry.
- Ability to multi-task and prioritize between projects
- Ability to work independently and as part of a team
- As mentioned above
We Value Diversity
At Wells Fargo, we believe in diversity, equity and inclusion in the workplace; accordingly, we welcome applications for employment from all qualified candidates, regardless of race, color, gender, national origin, religion, age, sexual orientation, gender identity, gender expression, genetic information, individuals with disabilities, pregnancy, marital status, status as a protected veteran or any other status protected by applicable law.
Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.
Candidates applying to job openings posted in US: All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
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