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About This Role
At Broadridge, we've built a culture where the highest goal is to empower others to accomplish more. If you’re passionate about developing your career, while helping others along the way, come join the Broadridge team.
Broadridge is hiring! We are seeking a Director of Data Science to lead Finance Transformation, Analytics and AI initiatives. Reporting to VP of ICS FP&A and Analytics, this role will partner within Technology, Business and Finance to elevate the organization’s centralized reporting and decision insights capabilities, improve the quality of financial data, strengthen forecasting and decision-making, and deliver measurable operational and financial outcomes.
Responsibilities:
- Develop a data science roadmap for financial systems that supports finance priorities and business objectives.
- Partner directly with finance leadership to identify high-value opportunities, define success measures, and communicate results and recommendations.
- Lead analytics, forecasting, anomaly detection, and automation initiatives across financial planning, reporting, and operational workflows.
- Work with financial systems and engineering teams to integrate data from relevant platforms and deploy reliable solutions into production.
- Establish standards for data quality, model validation, documentation, access controls, and responsible AI in finance use cases.
- Coach, and develop a high-performing data science team, while managing priorities across multiple stakeholders.
- Measure the financial and operational impact of initiatives and present findings to finance executives.
- Stay current with industry trends and advancements in data management technologies and practices.
Qualifications:
- 7–10 years of experience in data science, analytics, or a related quantitative field, including at least 3 years leading or working on major cross-functional initiatives.
- Substantial experience working with finance leadership and translating finance needs into data science solutions.
- Experience with financial data, reporting processes, and enterprise financial systems, such as ERP, planning, or financial reporting platforms.
- Strong skills in statistics, forecasting, experimentation, machine learning, Python, and SQL.
- Experience deploying and monitoring data products or models in production, with attention to accuracy, auditability, and controls.
- Ability to explain technical findings, trade-offs, and financial impact clearly to executive audiences.
- Demonstrated people leadership and cross-functional collaboration.
- Bachelor’s degree in quantitative, technical, finance, or related field, or equivalent practical experience.
- Experience with MLOps, generative AI evaluation, and data governance in a regulated environment.
- AWS application development experience, including Lambda, API Gateway, S3, RDS/DynamoDB, SNS/SQS, Cognito, CloudWatch, IAM, Secrets Manager, and related services desirable but not essential
Compensation Range: The salary range for this position is between $175,000 - $195,000.
Bonus Eligibility: Bonus Eligible
Broadridge considers various factors when evaluating a candidate's final salary including, but not limited to, relevant experience, skills, and education.
Benefits Information: Please visit www.broadridgebenefits.com for more information on our comprehensive benefit offerings.
All Colorado employees receive paid sick leave in compliance with the Colorado Healthy Families and Workplaces Act and other legally required benefits, as applicable. Apply by clicking the application link and submitting your information. The deadline to apply for this role is October 1, 2026
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We are dedicated to fostering a collaborative, engaging, and inclusive environment and are committed to providing a workplace that empowers associates to be authentic and bring their best to work. We believe that associates do their best when they feel safe, understood, and valued, and we work diligently and collaboratively to ensure Broadridge is a company—and ultimately a community—that recognizes and celebrates everyone’s unique perspective.
Use of AI in Hiring
As part of the recruiting process, Broadridge may use technology, including artificial intelligence (AI)-based tools, to help review and evaluate applications. These tools are used only to support our recruiters and hiring managers, and all employment decisions include human review to ensure fairness, accuracy, and compliance with applicable laws. Please note that honesty and transparency are critical to our hiring process. Any attempt to falsify, misrepresent, or disguise information in an application, resume, assessment, or interview will result in disqualification from consideration.
US applicants: Click here to view the EEOC "Know Your Rights" poster.
Disability Assistance
We recognize that ensuring our long-term success means creating an environment where everyone is welcome, where everyone's strengths are valued, and where everyone can perform at their best. Broadridge provides equal employment opportunities to all associates and applicants for employment without regard to race, color, religion, sex (including sexual orientation, gender identity or expression, and pregnancy), marital status, national origin, ethnic origin, age, disability, genetic information, military or veteran status, and other protected characteristics protected by applicable federal, state, or local laws.
If you need assistance or would like to request reasonable accommodations during the application and/or hiring process, please contact us at 888-237-7769 or by sending an email to [email protected].
Salary Context
This $175K-$195K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Broadridge, 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($185K) sits 13% below the category median. Disclosed range: $175K to $195K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Broadridge AI Hiring
Broadridge has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, New York, NY. Compensation range: $195K - $195K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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