Senior Lead Business Execution Consultant

About this role:

Wells Fargo is seeking a Senior Lead Business Execution Consultant.

Shared Services Operations - Operational Excellence team is seeking an Applied Data Scientist with a strong data science background to deliver traditional AI and Generative AI solutions for driving operational efficiencies, improve internal and external customer experiences with the use of AI. The candidate needs to possess a mix of technical expertise, creative problem-solving skills, and the ability to align AI solutions with business needs.

In this role, you will:

• Lead complex initiatives including creation, implementation, documentation, validation, articulation, and defense of highly advanced AI and Generative AI solutions.
• Deliver solutions for short and long-term objectives and provide analytical support for a wide array of business initiatives.
• Utilize neural network architectures, including transformer-based models such as GPT, BERT, and others for driving operational efficiencies.
• Familiarity with using diffusion models or multimodal AI depending on the use cases.
• Hands-on experience with fine-tuning, training, and deploying large language models (LLMs) in on-premises, cloud or hybrid environments.
• Strong hold on tokenization, embeddings, and model evaluation metrics.
• Present results of analysis, solution recommendations and AI-driven strategies for variety of business initiatives.

Required Qualifications:

• 7+ years of Business Execution, Implementation, or Strategic Planning experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications:

• 7+ years of Data Science experience, or equivalent demonstrated through one or a combination of the following: work experience, training, research and education.
• Bachelors/masters degree in a discipline such as Data Science, Computer Science, statistics, or mathematics.
• Expertise in Python and key/major frameworks like TensorFlow, PyTorch, and HuggingFace.
• Experience in using ML Ops tools (e.g., MLflow, Kubeflow) for scaling and deploying models in production.
• Experience working with on Google Cloud Platform and expertise in using the GCP services.
• Expertise in Scala for processing large-scale data sets for use cases.
• Proficiency in Java, JavaScript (Node.js) for back-end integrations and for building interactive AI-based web applications or APIs.
• Experience with SQL and NoSQL languages for managing structured, unstructured, and semi-structured data for AI and Generative AI applications.
• Critical thinking and strong problem-solving skills.
• Ability to learn the latest technologies and keep up with the trends in the Gen-AI space and apply to the business problems quickly.
• Ability to multi-task and prioritize between projects and able to work independently and as part of a team.
• Graduate degree from a top tier university (e.g., IIT, ISI, IIIT, IIM, etc.,) is preferred.

Job Expectations:

• Required to work individually or as part of a team on multiple AI and Generative AI projects and work closely with business partners across the organization. Mentor and coach budding Data Scientists on developing and implementing AI solutions.
• Perform various complex activities related to neural networks and transformer-based models.
• Provide analytical support for developing, evaluating, implementing, monitoring, and executing models across business verticals using emerging technologies.
• Expert knowledge on working on large datasets using SQL or NoSQL 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 codebase for reusability.
• Review and validate models and help improve the performance of the model under the preview of regulatory requirements.
• Work closely with technology teams to deploy the models to production.
• Prepare detailed documentations for projects for both internal and external use that comply with regulatory and internal audit requirements.