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About This Role
Lumen is the trusted network for the AI‑powered world, connecting people, data, and applications through our expansive fiber network and connected ecosystem. We enable secure, high‑performance connectivity across cloud, edge, and AI workloads for enterprises, governments, and communities.
At Lumen, you’ll work on infrastructure customers rely on today and build for what’s next, where performance, security, and resilience matter.
This is a high accountability environment where bold ideas drive real innovation for our customers, partners, and industry. The work is challenging, expectations are clear, and trust is built into how we operate. If you’re ready to take ownership, deliver meaningful impact, and help shape the future of AI‑ready connectivity, join us today.
The Role
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The Senior Manager of Financial Intelligence and Data Analytics is responsible for leading the design, implementation, and optimization of data systems and analytical processes that drive financial decision\-making. This role integrates artificial intelligence (AI) and automation to enhance data quality, speed, and insight delivery, while ensuring compliance and governance.
Work Location
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Fully remote position open to candidates based anywhere in the U.S.
The Main Responsibilities
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- Develop and execute a data strategy aligned with business goals, leveraging AI\-driven analytics to uncover trends, risks, and opportunities.
- Build and maintain robust data sets from multiple sources to support financial intelligence initiatives.
- Develop and maintain finance data models, dashboards, and reports to meet business and reporting needs.
- Apply statistical methods, modeling, and AI/ML techniques to detect anomalies, forecast performance, and support strategic planning.
- Lead and build a high\-performing technical team, providing direction, mentorship, and fostering a culture of innovation and continuous improvement.
- Translate complex financial data into clear, actionable insights for executives and stakeholders.
What We Look For in a Candidate
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- Bachelor’s degree in finance, accounting, business analytics, statistics, computer science, or related field; advanced degree preferred.
- 5\+ yrs in financial analytics, data management, or related roles; 3\+ yrs in a managerial capacity.
- Proficiency in SQL, Python, R, or similar for data analysis and automation.
- Experience with AI/ML frameworks.
- Familiarity with ETL/ELT tools, data warehouses, BI platforms (Tableau, Power BI, SAS), and automation tools (UiPath, Automation Anywhere).
- Strong quantitative aptitude, ability to interpret complex datasets, and present findings effectively.
- Leadership, strategic thinking, cross\-functional collaboration, and change management.
Compensation
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This information reflects the anticipated base salary range for this position based on current national data. Minimums and maximums may vary based on location. Individual pay is based on skills, experience and other relevant factors.
Location Based Pay Ranges
$132,232 \- $176,310 in these states: AL AR AZ FL GA IA ID IN KS KY LA ME MO MS MT ND NE NM OH OK PA SC SD TN UT VT WI WV WY
$138,844 \- $185,124 in these states: CO HI MI MN NC NH NV OR RI
$145,456 \- $193,940 in these states: AK CA CT DC DE IL MA MD NJ NY TX VA WA
Lumen offers a comprehensive package featuring a broad range of Health, Life, Voluntary Lifestyle benefits and other perks that enhance your physical, mental, emotional and financial wellbeing. We're able to answer any additional questions you may have about our bonus structure (short\-term incentives, long\-term incentives and/or sales compensation) as you move through the selection process.
Learn more about Lumen's:
- Benefits
- Bonus Structure
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Requisition \#: 342162
Life at Lumen
Life at Lumen is human and connected, even in a fast moving, AI‑focused organization. We set clear expectations and trust people to meet them. With real support and shared accountability, teams collaborate better, move faster, and deliver meaningful outcomes.
Our Lumen 8 behaviors guide how we interact, make decisions, and work together, shaping a culture built to perform and win.
To learn more about Life at Lumen and how we live the Lumen 8, please visit:
https://jobs.lumen.com/global/en/life\-at\-lumen
Background Screening
If you are selected for a position, there will be a background screen, which may include checks for criminal records and/or motor vehicle reports and/or drug screening, depending on the position requirements. For more information on these checks, please refer to the Post Offer section of our FAQ page. Job\-related concerns identified during the background screening may disqualify you from the new position or your current role. Background results will be evaluated on a case\-by\-case basis.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Equal Employment Opportunities
We are committed to providing equal employment opportunities to all persons regardless of race, color, ancestry, citizenship, national origin, religion, veteran status, disability, genetic characteristic or information, age, gender, sexual orientation, gender identity, gender expression, marital status, family status, pregnancy, or other legally protected status (collectively, “protected statuses”). We do not tolerate unlawful discrimination in any employment decisions, including recruiting, hiring, compensation, promotion, benefits, discipline, termination, job assignments or training.
Privacy Notice
Lumen is committed to protecting the privacy and security of personal information collected during the recruitment and hiring process. Our Privacy Notice explains how we collect, use, disclose, and protect applicant information, as well as how individuals may request access to or deletion of their personal data.
To review Lumen’s Privacy Notice, please visit:
https://jobs.lumen.com/global/en/privacy\-notice
Disclaimer
The job responsibilities described above indicate the general nature and level of work performed by employees within this classification. It is not intended to include a comprehensive inventory of all duties and responsibilities for this job. Job duties and responsibilities are subject to change based on evolving business needs and conditions.
In any materials you submit, you may redact or remove age\-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.
Please be advised that Lumen does not require any form of payment from job applicants during the recruitment process. All legitimate job openings will be posted on our official website or communicated through official company email addresses. If you encounter any job offers that request payment in exchange for employment at Lumen, they are not for employment with us, but may relate to another company with a similar name.
Salary Context
This $132K-$193K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Lumen, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($163K) sits 9% below the category median. Disclosed range: $132K to $193K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Lumen AI Hiring
Lumen has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $155K - $278K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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