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About This Role
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BBVA is a global company with more than 160 years of history that operates in more than 25 countries where we serve more than 80 million customers. We are more than 121,000 professionals working in multidisciplinary teams with profiles as diverse as financiers, legal experts, data scientists, developers, engineers and designers.
About the job:
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Position Summary
We are seeking an experienced Associate, AI Solutions \& Analytics to lead the adoption of data and AI capabilities across the organization while developing scalable data products that accelerate business decision\-making. This leadership role designs solutions, develops analytical capabilities, and defines the functional scope of initiatives by working closely with business teams to translate their needs into scalable AI products and capabilities.
The ideal candidate is a data specialist with 10\+ years of experience in analytics, business intelligence, or data engineering, with a proven ability to lead complex initiatives, influence senior stakeholders, develop self\-service analytics capabilities, and deliver measurable business impact.
Key Responsibilities
- Lead data and AI enablement initiatives, including onboarding, training, and adoption programs for business teams.
- Design and deliver business\-ready data products and self\-service analytics solutions for both technical and non\-technical users.
- Partner with business and technology leaders to define KPIs, identify opportunities, and translate business needs into scalable data solutions.
- Build and optimize data models, analytical datasets, dashboards, and reporting capabilities.
- Support enterprise transformation initiatives by embedding data and AI into business processes and operating models.
- Promote data governance, quality, and analytics best practices while balancing speed and business value.
- Mentor team members and foster a culture of data\-driven decision\-making and continuous improvement.
Required Qualifications
- Associate\-level experience or 10\+ years of progressive experience in Data Analytics, Business Intelligence, Data Engineering, or related disciplines.
- Proven experience leading enterprise\-wide data initiatives and delivering measurable business impact in complex organizations.
- Experience implementing scalable data processes and self\-service analytics platforms serving large user communities.
- Strong executive presence with exceptional stakeholder management, communication, and cross\-functional leadership skills.
- Authorization to work in the United States without current or future employer sponsorship is required.
Technical Skills Required
- Expert SQL
- Advanced Python
- Strong understanding of modern data architecture and analytics engineering
Languages
- English – Full Professional Proficiency
- Spanish – Professional Proficiency preferred
Preferred Profile
The ideal candidate is a strategic data leader with a proven track record of building high\-performing analytics organizations, delivering data products, and accelerating the adoption of data and AI capabilities. Experience leading cross\-functional transformation initiatives within global organizations and influencing senior executives is highly valued.
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.
With respect to this position in our New York Office, the expected base salary ranges from $100,000 to $130,000\. It is not typical for offers to be made at or near the top of the range. Salary offers are based on a wide range of factors including relevant skills, training, experience, education, and, where applicable, certifications obtained. Market and organizational factors are also considered. In addition to salary and a generous employee benefits package, successful candidates are eligible to receive a discretionary bonus.
- Employment eligibility to work with BBVA in the U.S. is required as the company will not pursue visa sponsorship for these positions
Legal requirements
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It is not typical for offers to be made at or near the top of the range. Salary offers are based on a wide range of factors including relevant skills, training, experience, education, and, where applicable, certifications obtained. Market and organizational factors are also considered. In addition to salary and a generous employee benefits package, successful candidates are eligible to receive a discretionary bonus.
Pay Transparency Policy Statement
The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor's legal duty to furnish information (41 C.F.R. 60\-1\.35 (c)).
Individuals with Disabilities
BBVA USA, BBVA Securities Inc., and BBVA S.A. New York Branch invite all interested and qualified applicants to apply for employment opportunities. If you are a U.S.\-based job seeker with a disability who is unable to use our online tools to search and apply for jobs, please contact us by emailing: [email protected] or by calling toll\-free (in the U.S.) 1\-844\-664\-9275\. Please indicate the specific type of assistance needed\*.
- The disability access telephone line and email address are reserved solely for job seekers with disabilities requesting accessibility assistance or an accommodation. Please do not call about the status of your job application if you do not require accessibility assistance or an accommodation. Messages left for other purposes, such as following up on an application or non\-disability related or technical issues, will not receive a response.
EEO Statement
BBVA USA, BBVA Securities Inc., and BBVA S.A. New York Branch have a firm and unwavering policy to provide equal employment opportunity without regard to age, citizenship, color, disability, ethnic origin, gender, gender identity and expression, marital status, nationality, national origin, race, religion, sexual orientation, genetic predisposition, protected veteran status, or any other status or classification protected by federal, state or local law. This policy includes all job groups, classifications and organizational units. With regard to employment, this policy extends to applicants and covers our recruiting, hiring, promotion, transfer, demotion, discipline, termination, benefits, compensation and training practices as well as social and recreational activities.
View the " EEO is the Law " \& " View the EEO is the Law Supplement Poster " poster. BBVA USA, BBVA Securities, Inc., and BBVA NY are equal opportunity and affirmative action employer.
Salary Context
This $100K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BBVA, 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($115K) sits 46% below the category median. Disclosed range: $100K to $130K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
BBVA AI Hiring
BBVA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $130K - $130K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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