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About This Role
Why Wells Fargo
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About this role
Wells Fargo Enterprise Complaints, Remediations and Loudspeaker Analytics (ERA) is seeking a Senior Data Science Consultant focused on advanced analytics and AI solutions supporting voice‑of‑customer insights, risk identification, and operational decisioning. This role is strongly oriented toward applied Generative AI, with a primary focus on designing, experimenting with, and evaluating LLM‑enabled systems that operate on large volumes of unstructured customer interaction data.
The consultant will own the end‑to‑end experimentation lifecycle for GenAI use cases — including prompt and agent design, iterative testing, error analysis, tuning, and evaluation — while leveraging traditional machine learning and NLP techniques where appropriate to support or augment GenAI solutions. The role emphasizes practical execution, rapid prototyping, and disciplined evaluation to ensure outputs are reliable, explainable, and suitable for use in risk‑aware, human‑in‑the‑loop decision environments.
In this role, you will
- Lead hands‑on Generative AI experimentation, including prompt engineering, prompt library development, and agent‑style workflows that support voice‑of‑customer understanding, issue identification, and decision support.
- Design and execute systematic testing of LLM outputs across large collections of historical customer interaction data, evaluating behavior across tasks, data conditions, and edge cases.
- Conduct deep error analysis of GenAI outputs, identifying hallucinations, weak or missing evidence, false positives, false negatives, and ambiguity, and translate findings into targeted prompt and system improvements.
- Develop and apply GenAI evaluation frameworks, including rule‑based heuristics, statistical indicators, and LLM‑as‑a‑Judge techniques, to assess output quality, consistency, and risk. Build and refine confidence and uncertainty scoring mechanisms for LLM decisions to support prioritization and secondary human review in higher‑risk scenarios.
- Apply machine learning and NLP models where appropriate to complement GenAI solutions, such as feature extraction, classification, clustering, or signal generation.
- Analyze complex structured and unstructured datasets to generate hypotheses, surface emerging risks, and identify opportunities where GenAI can augment or automate decision workflows.
- Collaborate closely with product teams, engineers, and business stakeholders to align GenAI experimentation with operational workflows, risk tolerance, and real‑world constraints.
- Produce clear documentation of prompts, experiments, evaluation methods, and findings to ensure transparency, repeatability, and knowledge sharing.
Communicate GenAI behaviors, trade‑offs, limitations, and risks effectively to non‑technical stakeholders, helping set appropriate expectations for usage.
- May mentor teammates by sharing best practices related to GenAI experimentation, evaluation, and responsible deployment.
Required Qualifications
- 4\+ years of data science 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
- Strong hands‑on experience with Python‑based experimentation and analytics workflows, working with large structured and unstructured text datasets; SQL proficiency required, SAS/Teradata a plus.
- Demonstrated practical experience building and testing Generative AI solutions, including prompt engineering, prompt tuning, task decomposition, and agent‑style workflows using LLMs.
- Proven ability to perform LLM evaluation and error analysis, including hallucination detection, output quality assessment, and false positive/false negative analysis.
- Experience designing or implementing confidence, uncertainty, or risk‑scoring mechanisms for GenAI outputs to support review and escalation decisions.
- Familiarity with Machine Learning and NLP modeling techniques, and the ability to apply them selectively to complement GenAI‑driven approaches.
- Ability to design repeatable testing methodologies, benchmarks, and success metrics for GenAI systems operating in risk‑sensitive environments.
- Strong communication skills, with the ability to clearly explain GenAI behaviors, limitations, and experimental findings to both technical and non‑technical audiences.
- Experience producing high‑quality documentation covering prompts, experiments, evaluation methods, and system behaviors.
- Comfortable operating in ambiguous problem spaces, with an execution mindset focused on experimentation, learning, and continuous improvement.
- Strong statistical background and deep understanding of statistical methods for extracting insight from large, complex datasets.
- Hypothesis driven, investigative or “detective like” approach to identifying anomalies, edge cases, unexpected behaviors, and weak signals in both data and model outputs.
- Comfort applying statistical reasoning to error analysis, uncertainty estimation, and validation of GenAI and ML driven results.
Job Expectations:
- Ability to travel up to 10% of the time.
- This position is NOT eligible for Visa sponsorship.
- Ability to work on site per Wells Fargo's standard operating model in one of the listed locations.
Posting Locations:
- CHANDLER, AZ
- SAN ANTONIO, TX
- WEST DES MOINES, IA
- MINNEAPOLIS, MN
- CHARLOTTE, NC
- IRVING, TX
The Chief Operating Office Functions adhere to a location strategy; therefore, your candidacy may be determined based on your current location. Remote work locations are not available for these roles, so if you are not in a location listed on the posting, you must commit to self\-relocation within an agreed upon timeframe.
Pay Range
Reflected is the base pay range offered for this position. Pay may vary depending on factors including but not limited to demonstrated examples of prior performance, skills, experience, or work location. Employees may also be eligible for incentive opportunities.
$119,000\.00 \- $206,000\.00Benefits
Wells Fargo provides eligible employees with a comprehensive set of benefits, many of which are listed below. Visit Benefits \- Wells Fargo Jobs for an overview of the following benefit plans and programs offered to employees.
- Health benefits
- 401(k) Plan
- Paid time off
- Disability benefits
- Life insurance, critical illness insurance, and accident insurance
- Parental leave
- Critical caregiving leave
- Discounts and savings
- Commuter benefits
- Tuition reimbursement
- Scholarships for dependent children
- Adoption reimbursement
Posting End Date:
19 Jun 2026* *Job posting may come down early due to volume of applicants.*
We Value Equal Opportunity
Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
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.
Applicants with Disabilities
To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.
Drug and Alcohol Policy
Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.
Wells Fargo Recruitment and Hiring Requirements:
a. Third\-Party recordings are prohibited unless authorized by Wells Fargo.
b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.
Salary Context
This $119K-$206K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Wells Fargo, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($162K) sits 10% below the category median. Disclosed range: $119K to $206K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Wells Fargo AI Hiring
Wells Fargo has 23 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Safety, AI Product Manager. Positions span Charlotte, NC, US, New York, NY, US, Irving, TX, US. Compensation range: $140K - $305K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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