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About This Role
Welcome to Destination Pet. We are One Pack- a united team working together to elevate the love and lives of pet families. Our pet-obsessed professionals create a connected care network of trustworthy partners who bring creative innovation every day. We are revolutionizing the pet industry. Join us!
Destination Pet is launching a new, PE-backed business unit focused on building a differentiated data platform for the pet health and pet services ecosystem.
This new business unit will collect, organize, analyze, and commercialize data related to dogs and cats through veterinary partners, pet care centers, boarding and daycare facilities, grooming locations, wellness providers, and other pet care channels.
We are seeking a Director of Data Science to help build the analytical foundation of this business from the ground up. This is a unique opportunity for someone who wants to operate in a newly formed, high-growth business while benefiting from the backing, scale, and resources of an established, private-equity-backed platform.
The ideal candidate is a hands-on data science leader who can work with complex and fragmented data, build commercially valuable insights, and help turn pet health and pet care data into scalable products. We are looking to add a Data Science Director to our team!
LOCATION: Remote US
TRAVEL: Less than 10%
REPORTS TO: Chief Executive Officer
PAY: $180 K -$210K + 20 %Bonus
Destination Pet does not provide any Visa Sponsorship.
Role Overview:
Destination Pet is launching a new, PE-backed business unit focused on building a differentiated data platform for the pet health and pet services ecosystem.
This new business unit will collect, organize, analyze, and commercialize data related to dogs and cats through veterinary partners, pet care centers, boarding and daycare facilities, grooming locations, wellness providers, and other pet care channels.
We are seeking a Director of Data Science to help build the analytical foundation of this business from the ground up. This is a unique opportunity for someone who wants to operate in a newly formed, high-growth business while benefiting from the backing, scale, and resources of an established, private-equity-backed platform.
The ideal candidate is a hands-on data science leader who can work with complex and fragmented data, build commercially valuable insights, and help turn pet health and pet care data into scalable products.
CORE RESPONSIBILITES:
- Help define and build the data science strategy for a newly formed pet health data business unit.
- Analyze large, complex, and multi-source datasets related to pet health, veterinary care, pet services, customer behavior, and service utilization.
- Build predictive models, segmentation models, forecasting tools, and other analytical solutions to identify trends, patterns, and commercial opportunities.
- Partner with product, technology, commercial, operations, and executive stakeholders to identify data products and insights that can be monetized.
- Support the development of data products that may be sold to external customers, partners, or industry stakeholders.
- Work with internal and external data sources, including veterinary partners, pet care centers, customer systems, and third-party datasets.
- Lead cross-functional efforts in data cleaning, normalization, deduplication, taxonomy creation, and quality control to ensure data integrity and support ongoing monitoring.
- Translate complex analytical findings into clear, actionable insights for non-technical audiences.
- Help establish best practices for responsible data use, privacy, governance, de-identification, and data quality.
- Operate comfortably in an entrepreneurial environment where business models are still being developed.
- Drive the creation of novel, statistically differentiated predictive models, supported by a strong track record in research and data science.
- Help shape the future structure, tools, and capabilities of the data science function as the business scales.
REQUIREMENTS:
Education & Experience:
- Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, Economics, Bioinformatics, or a related quantitative field.
- 6-10+ years of experience in data science, advanced analytics, machine learning, statistical modeling, or a related quantitative role.
- Strong proficiency in Python and/or R.
- Strong SQL skills and experience working with relational databases and large datasets.
- Experience working with messy, fragmented, or multi-source data and turning it into usable analytical assets.
- Demonstrated experience building predictive models, segmentation frameworks, forecasting models, machine learning models, or other advanced analytical solutions.
- Strong understanding of data quality, feature engineering, statistical inference, experimentation, model validation, and analytical best practices.
- Ability to communicate complex analysis clearly to executive, commercial, product, and non-technical stakeholders.
- Comfort working in a newly formed business, startup-like environment, or high-growth setting with ambiguity and evolving priorities.
Preferred Qualifications
- Master's degree or PhD in a quantitative discipline.
- Experience in a newly formed company, startup, venture-backed company, PE-backed company, or corporate incubation environment.
- Experience building data products, commercial analytics products, data platforms, data marketplaces, or monetizable insight products.
- Experience in healthcare, veterinary medicine, pet care, life sciences, insurance, consumer health, retail, marketplace businesses, or multi-site services.
- Experience working with regulated, sensitive, privacy-conscious, or consumer data environments.
- Familiarity with privacy-preserving analytics, de-identification, aggregation, consent frameworks, and responsible data-use practices.
- Experience with cloud data platforms such as Snowflake, Databricks, AWS, GCP, Azure, or similar.
- Experience with BI and visualization tools such as Tableau, Power BI, Looker, Sigma, or similar.
- Experience partnering with product, engineering, commercial, legal, operations, and executive teams.
Ideal Candidate Profile
- Builder mindset with the ability to create structure in an early-stage environment.
- Commercially curious and interested in turning data into revenue-generating products.
- Comfortable working hands-on while also helping define strategy and future team capabilities.
- Strong business judgment and ability to prioritize the highest-value analytical opportunities.
- Comfortable operating in a PE-backed environment with a focus on speed, accountability, value creation, and measurable outcomes.
- Able to work independently in a fully remote environment while collaborating effectively across functions.
- Passionate about the opportunity to transform pet health and pet care data into meaningful insights.
Skills
- Data science and machine learning
- Predictive modeling and statistical analysis
- SQL, Python, and/or R
- Data cleaning, normalization, and quality control
- Data product development
- Commercial analytics and data monetization
- Executive-level storytelling and insight presentation
- Privacy-aware and ethical data use
- Cloud data platforms
- Startup, new business unit, or PE-backed operating experience
- Cross-functional collaboration
Why Join Us
- Join at the ground floor of a newly formed, PE-backed business unit.
- Help build a new data platform in the pet health and pet services ecosystem.
- Work on a commercially meaningful opportunity with the potential to shape a new category of pet health intelligence.
- Build the data science foundation, analytical roadmap, and future data products from the beginning.
- Operate with startup-like agility while benefiting from the backing of an established platform.
- Help turn fragmented pet care data into insights, products, and long-term enterprise value.
PERKS:
- Health, vision, dental, long & short-term disability, and Life insurance
- A Startup mentality company with stable funding
- 401k match
- Pet Care discounts
- Flexible Time Off
- Opportunity to grow your career with a network of like-minded professionals
Destination Pet LLC affords equal employment opportunities to employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, genetic information, age, disability or any other characteristic protected by federal, state or local law. Equal Opportunity Employer Minorities/Women/Veterans/Disabled.
Destination Pet LLC endeavors to make our employment website and application process accessible to all users and applicants. In doing so, we provide reasonable accommodations to applicants with disabilities. If you have a disability and require a reasonable accommodation or assistance completing the application process, please contact a member of our recruitment team.
At Destination Pet, we're all about creating a workplace that's bursting with diversity and inclusivity. We don't play favorites or judge folks based on skin color, religion, gender, age, disability, or any other protected characteristics. Our hiring decisions are solely based on what you can bring to the team. So, we're throwing the door wide open for all like-minded candidates who believe in celebrating and honoring diversity and inclusion. Ready to join our pack?
Salary Context
This $180K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Destination Pet, LLC, 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 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($195K) sits 8% below the category median. Disclosed range: $180K to $210K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Destination Pet, LLC AI Hiring
Destination Pet, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $210K - $210K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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