Director, Data Science

Destination Pet, LLC

1d•$180,000 - $210,000•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.