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About This Role
Summary:
Overview
Every year, millions of animals enter shelters across the United States \- and the data behind their stories has the power to transform how shelters and communities care for them. As Senior Manager of Data Science at Shelter Animals Count (a Program of the ASPCA), you'll be at the center of the nation's most comprehensive effort to collect, analyze, and share animal sheltering data. Your work will directly shape the estimates, models, and insights that guide policy, improve shelter operations, and ultimately improve animal welfare. If you're a data scientist who loves data analysis and machine learning and wants your skills to drive real\-world impact for animals and the communities that serve them, this is your role.
The Senior Manager, Data Science reports to the Senior Director, Research \& Data Science, and will work closely with other members of the Shelter Animals Count (SAC) and broader Strategy team. They will work on data analysis projects and refine and maintain the machine learning models that power SAC's national estimates of shelter animal intakes, outcomes, and population trends. They will also work closely with a diverse group of stakeholders, external partners, interns, contractors, and vendors to execute key programmatic objectives as outlined in SAC’s plan. They will bring a high level of expertise and integrity in data science, analytics, and reporting.
Who We Are
Shelter Animals Count (SAC) is a nationally recognized brand dedicated to collecting, analyzing, and sharing data on animal sheltering across the United States and Canada, and will join the ASPCA’s Strategy Team. The Strategy department has five verticals: Strategy and Planning, Research, Impact Measurement and Data Science, Project Management Office, and SAC. The Strategy team uses strategic and data\-driven processes to guide organizational direction, drive effective decision\-making, and deliver actionable, evidence\-based insights to shelters and communities. Strategy serves as a thought partner to ASPCA departments; facilitates collaboration among departments; and connects the dots to maximize efforts to drive our goals forward.
Shelter Animals Count, established in 2012, is dedicated to collecting and sharing reliable shelter data to improve welfare and outcomes for animals and communities. By serving as a neutral, industry\-wide data collection entity, Shelter Animals Count provides valuable insights into shelter operations, trends in animal welfare, intake and outcomes, and the challenges faced by shelters, rescues, and communities. Shelter Animals Count collaborates with organizations of all sizes to encourage data submission, fostering a comprehensive understanding of the state of animal welfare. This data\-driven approach supports evidence\-based decision\-making, ultimately helping to improve welfare and outcomes for animals, guide policy, and increase transparency within the animal welfare community.
What You’ll Do
You will lead SAC's data science and analytical work \- building and improving estimation and forecasting models, ensuring data quality and methodological rigor, and translating complex datasets into actionable insights for partners, shelters, and the public. You'll also collaborate on data reporting, data visualization/dashboarding and peer\-reviewed research, and represent SAC's data expertise both internally and at national conferences.
This role involves hands\-on work with Shelter Animals Count's database (for example, frequent analysis of datasets including 20M\+ animals, many parameters). The ideal candidate should be comfortable working at scale, building and validating statistical and machine learning models, and owning the full analytical pipeline from raw data to published estimates.
The Senior Manager, Data Science, reports directly to the Senior Director, Research \& Data Science and has 0 direct reports.
Where and When You’ll Work
This remote\-based position (which requires travel, as described below) is open to all eligible candidates based within the United States. The schedule for this position is generally Monday\-Friday 9am\-5pm (flexibility needed, as some early mornings, evenings, and weekends will be required.)
Ability and willingness to travel up to 10% annually as needed.
What You’ll Get
Compensation
The target hiring range is based on where the employee works, which for remote roles is the employee’s primary location of residence, and its respective cost of labor. You can view which zone applies to you based on your location (aspca.app.box.com/v/aspcazonetable). For questions regarding locations not on the list, please send an email to Careers@aspca.org for more information.
Starting pay for the successful applicant will depend on a variety of factors, including but not limited to education, training, experience, location, business needs, internal equity, market demands or budgeted amount for the role. The target hiring range is for new hire offers only, and compensation may increase beyond the maximum hiring range based on performance over time. The maximum of the hiring range is reserved for candidates with the highest qualifications and relevant experience. The expected hiring salary ranges for this role are set forth below and may be modified in the future.
- Zone 1: $93,000\-$97,000 annually
- Zone 2: $102,000\-$107,000 annually
- Zone 3: $112,000\-$117,000 annually
Benefits
At the ASPCA, you don’t have to choose between your passion and making a living. Our comprehensive benefits package helps ensure you can live a rewarding life at work and at home. Our benefits include, but are not limited to:
- Affordable health coverage, including medical, employer\-paid dental, and optional vision coverage.
- Flexible time off that includes vacation time, sick and bereavement time, paid parental leave, 10 company paid holidays, and paid personal time off that allows you even more flexibility to observe the days that mean the most to you.
- Competitive financial incentives and retirement savings, including a 401(k) plan with generous employer contributions — we match dollar for dollar up to 4% and provide an additional 4% contribution toward your future each year.
- Robust professional development opportunities including classes, on\-the\-job training, coaching and mentorship with industry\-leading peers, internal mobility, opportunities to support in the field, and so much more.
Responsibilities:
Responsibility buckets are listed in general order of importance, and include but are not limited to:
### Data Science and Analysis
- Serve as a subject matter expert on national datasets, providing data and analytical support to advance SAC deliverables and industry collaborations.
- Advance innovation, creative problem solving, and cross\-departmental collaboration to improve outcomes and processes.
- Apply best practices in data science, including univariate and multivariate statistical techniques and machine learning, to derive insights from large and complex datasets to inform understanding and decision\-making.
- Oversee, enhance, and maintain SAC’s machine learning estimation and forecasting models.
- Identify, evaluate, and report on new data sources to strengthen and broaden insights into animal welfare, sheltering, and community trends.
- Demonstrate intellectual curiosity by proactively exploring data for additional insights beyond initial inquiries.
- Automate data analysis workflows to enhance efficiency and reproducibility.
- Clearly annotate and document all code to support transparency, collaboration, and long\-term maintainability.
- Collaborate on data analysis for peer\-reviewed research projects, contributing to rigorous methodology, interpretation, and publication.
### Reporting, Insights \& Communication
- Partner with the broader SAC program and ASPCA team to fulfill media and partner data requests, ensuring timely, accurate statistics, clear methodology explanations, and actionable insights.
- Develop, build, and maintain reports, Tableau dashboards, and processes to monitor data quality, integrity, and overall efficiency.
- Build strong, supportive working relationships across teams and departments to advance shared goals.
- Adapt to change with flexibility, curiosity, and a solutions\-oriented mindset.
- Represent SAC data and reporting at conferences throughout the year, providing in\-person data analytics expertise.
### Data Governance \& Quality
- Adhere to and promote data governance policies, business rules, and definitions to ensure consistent program evaluation and measurement.
- Support across the SAC team in implementing improved data governance processes, including workflow design, business rules, role permissions, quality assurance, approval, and publishing protocols.
- Streamline and automate manual processes to ensure the most current data is readily available to business users.
### Supervision of Data Science Interns
- Occasionally recruit, train and manage Data Science interns on short\- and long\-term data projects, providing mentorship and guidance while continuously developing collaborative workflows to support ongoing priority initiatives.
Qualifications
- Demonstrated ability to analyze and draw conclusions from large volumes of data and information with a high level of accuracy and integrity.
- Extensive knowledge of and experience with data analysis tools (Python, R, SQL, etc.)
- Extensive knowledge of and experience with data visualization tools (Tableau/Tableau Prep/Power BI, or similar)
- Demonstrated knowledge and experience applying statistical and machine learning techniques \- including descriptive, inferential, estimation, and forecasting methods \- to generate insights and support data\-driven decision\-making.
- Experience with or enthusiasm for using AI\-assisted development tools (e.g., Claude Code, GitHub Copilot) to accelerate data science workflows.
- Self‐motivated individual capable of working independently; managing priorities across multiple, concurrent programs and initiatives; and remaining curious about the data to stretch beyond questions asked.
- Ability to exemplify ASPCA’s core values and behavioral competencies
Education and Work Experience
- Advanced degree in Data Analytics, Data Science, or related field preferred or equivalent work experience.
- 5 years of combined experience in statistical analysis, machine learning, programming for data science, data interpretation, data reporting and research within academic, non\-profit, business, and/or related fields required.
- Experience working with large\-scale datasets (millions of records) and deriving meaningful insights from complex, multi\-dimensional data required.
- Familiarity with animal welfare and animal sheltering data collection and analysis is preferred.
- Experience with data visualization and analytics tools required (Tableau/Tableau Prep/Power BI, SQL, Python, R, or similar) required
Language:
English (Required)Education and Work Experience:
Our EEO Policy:
The ASPCA is an Equal Employment Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, national origin, ancestry, gender, gender identity or expression, age, marital or domestic partner status, citizenship status, sexual orientation, disability, genetic information, military or veteran status, or any other characteristic protected by applicable federal, state or local laws, regulations or ordinances.
Applicants with disabilities may be entitled to a reasonable accommodation under the terms of the Americans with Disabilities Act and certain state or local laws. A reasonable accommodation is a change in the ASPCA’s standard application process, which will ensure an equal employment opportunity without imposing undue hardship on the ASPCA. Please inform the ASPCA’s People Team if you need an accommodation in order to complete any forms or to otherwise participate in the application process.
Individuals seeking employment are considered without regards to race, color, creed, religion, sex, national origin, ancestry, gender, gender identity or expression, age, marital or domestic partner status, citizenship status, sexual orientation, disability, genetic information, military or veteran status, or any other characteristic protected by applicable federal, state or local laws, regulations or ordinances.
ASPCA is an Equal Opportunity Employer (M/F/D/V).
About Us:
The ASPCA was founded in 1866 on the belief that animals are entitled to kind and respectful treatment by humans and must be protected under the law. As a 501(c)(3\) not\-for\-profit corporation with more than two million supporters nationwide, the ASPCA is committed to preventing cruelty to dogs, cats, equines, and farm animals throughout the United States.
The ASPCA is headquartered in New York City, where we maintain a full\-service animal hospital, spay/neuter clinic, mobile spay/neuter and primary pet care clinics, a rehabilitation center for canine victims of cruelty, kitten nursery, adoption center, and two community veterinary centers.
The ASPCA also operates programs and services that extend nationwide. We assist animals in need through on\-the\-ground disaster and cruelty interventions, behavioral rehabilitation, animal placement, legal and legislative advocacy, and the advancement of the sheltering and veterinary community through research, training, and resources.
At the ASPCA, we are committed to fostering a collaborative and compassionate culture and we welcome all voices to contribute to our lifesaving mission. Our staff represent a vast array of backgrounds and diversity dimensions, bringing with them valuable experiences and perspectives. They join the ASPCA to learn, grow, and continually do their best work on behalf of animals. We are inspired by our staff, partners, and the communities we support across the country who work to improve animal lives. We are committed to diversity, equity, and inclusion at the ASPCA because it elevates our organizational culture, aligns with our Core Values, and enables us to move further and faster toward the ASPCA’s vision – that all animals live good lives; valued by society, protected by its laws, and free from cruelty, pain and suffering.
Your Employee Rights Under the Family and Medical Leave Act:
Pursuant to regulations of the Family and Medical Leave Act (FMLA), we provide this notice to applicants about eligible employees’ protected leave for certain reasons. Click on the link to learn more.
Applicants that are residents of Colorado and Oregon: Per CO Senate Bill 23\-058 and ORS 659A.030, we cannot generally request or require any age\-related information (i.e., age, DOB, attendance or graduation dates from an educational institution) on an initial employment application; this includes any age\-related inquiries through completion of the initial interview. We can require/request additional application materials; if those materials contain any age\-related information, an applicant should redact the information before submitting an initial employment application.
Indiana Applicants: Pursuant to Indiana law, we are providing notice that it is an unlawful employment practice for an employer to discriminate against a prospective employee on the basis of status as a veteran by: (1\) refusing to employ an applicant for employment on the basis that the applicant is a veteran of the armed forces of the United States; or (2\) refusing to employ an applicant for employment on the basis that the applicant is a member of the Indiana National Guard or a member of a reserve component. Should you feel that you were a victim of discrimination on the basis of veteran status, please let us know at careers@aspca.org. Alternatively, you can file a complaint with the following agencies at any time: Indiana Civil Rights Commission (ICRC) 100 North Senate Avenue, Room N103, Indianapolis, IN 46204; Office: (317\) 232\-2600 \| Toll Free: (800\) 628\-2909; Hearing Impaired: (800\) 743\-3333 \| Fax: (317\) 232\-6580; E\-mail: icrc@crc.in.gov \| Website: www.in.gov/icrc. Equal Employment Opportunity Commission \- Indianapolis Field Office; 115 W. Washington Street South Tower Suite 600; Indianapolis, IN 46204; Phone: 463\-999\-1240; Fax: 317\-226\-7953; TTY: 1\-800\-669\-6820;ASL Video Phone: 844\-234\-5122\.
Massachusetts Applicants: Per Massachusetts law, we are providing notice that it is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Philadelphia Applicants: You may view your rights under the Fair Criminal Record Screening Standards Act here.
Salary Context
This $112K-$117K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At ASPCA, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($114K) sits 31% below the category median. Disclosed range: $112K to $117K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
ASPCA AI Hiring
ASPCA has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Asheville, NC, US, Remote, US. Compensation range: $117K - $210K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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