Consumer Data Scientist

Covetrus•Dublin, OH

2d•Remote

About The Position

Covetrus is looking for a Consumer Data Scientist to drive and support advanced analytics and applied data science that accelerates Covetrus’ consumer-facing marketing, product experiences, and strategic business initiatives. As part of Covetrus, you will work with one of the largest in-clinic and online pet parent behavior datasets in the world. You will design, build, and operationalize models and analytical frameworks that improve new consumer acquisition, drive retention, influence product and brand preference, and enhance the experience of our consumer-facing products. You will partner closely with Marketing, Product, and Commercial leaders to translate ambiguous business questions into clear analytical problems, develop data-driven solutions, and communicate recommendations that shape strategy. You will contribute to a culture of collaboration, innovation, and continuous improvement as you provide insight into the business and drivers of consumer behavior, loyalty, and product preferences.

Requirements

Bachelor’s degree in Statistics, Mathematics, Economics, Computer Science, Engineering, or a related quantitative field required.
3+ years of experience in applied data science, machine learning, or advanced analytics in a consumer, digital, or eCommerce environment.
Demonstrated experience supporting end-to-end modeling projects—from problem framing and data preparation through model development, validation, deployment, and performance monitoring.
Proven experience in predictive modeling and machine learning, including techniques such as regression, regularization, tree-based methods (e.g., random forest, gradient boosting), classification, clustering/segmentation, time-series forecasting, and/or uplift modeling.
Proficiency in SQL and working with large-scale, complex datasets in modern data warehouses; experience with Snowflake is preferred.
Strong programming skills in Python and/or R for data science.
Experience designing and building data pipelines or automated analytical workflows and reporting tools in partnership with data engineering or BI teams.
Demonstrated track record of delivering actionable predictive insight that drives measurable business impact in a consumer or eCommerce context.
Strong analytical and quantitative skills, including the ability to use customer research, experimentation, and performance data to optimize marketing and product decisions.
Experience designing, executing, and analyzing experiments (A/B tests) and/or using causal inference techniques to estimate incremental impact.
Proven ability to convey key insights from complex analyses in clear, concise business terms, including building compelling presentations and data visualizations (experience with BI tools such as Tableau or Power BI is a plus).
Understanding of how pricing, promotion, and brand impressions influence consumer loyalty, brand preference, and lifetime value, and ability to synthesize insights that inform marketing and product strategy.
Strong skills in Excel and comfort working with relational databases and cloud-based data ecosystems.
Hands-on experience using machine learning and AI for analytics and decisioning is required; experience with modern ML/AI tooling (e.g., model orchestration, feature stores, experimentation platforms) is a plus.
Aptitude and interest in learning new technologies, methods, and tools, and in staying current with the evolving data science and marketing analytics landscape.
Strong collaboration and stakeholder management skills, with the ability to build trusted partnerships across Marketing, Product, Commercial, and Technology teams.

Nice To Haves

Advanced degree (Master’s or PhD) in a quantitative discipline a plus.
Understanding of the online marketing ecosystem and channels (e.g., direct, social media, display, affiliate, email, SEM, SEO), customer segmentation, and CRM/database marketing methods is a plus.
Experience with BI tools such as Tableau or Power BI is a plus.
Experience with modern ML/AI tooling (e.g., model orchestration, feature stores, experimentation platforms) is a plus.

Responsibilities

Drive and support the development, validation, and deployment of predictive and machine learning models (e.g., propensity, churn, LTV, recommendations, next best action, marketing response) that support data-driven marketing and business decisions for the Prescription Management and other consumer businesses.
Design and analyze experiments (A/B and multivariate tests) and apply causal inference methods to measure the impact of marketing, pricing, and product changes on key consumer outcomes.
Identify, scope, and prioritize high-impact data science opportunities that shape consumer experience, drive adoption, increase loyalty, influence brand preference, and deepen consumer engagement.
Maintain a deep understanding of consumer behavior, competitive trends, and business drivers; proactively surface insights and hypotheses that inform strategy and roadmap decisions.
Analyze key trends and build robust statistical and machine learning models to predict consumer behavior using strong statistical rigor and best practices.
Establish scalable, efficient, and automated processes for large-scale data preparation, model training, scoring, and monitoring in partnership with Data Engineering and Analytics teams.
Partner with teams across the prescription management and broader consumer business to design, develop, and maintain forecasts, analytical tools, and dashboards that highlight key business drivers and model outputs.
Translate complex analytical findings into clear, compelling stories and recommendations tailored to non-technical stakeholders, driving alignment and action.
Contribute to data science standards, best practices, and documentation, and mentor team members in advanced analytics and modeling techniques.
Collaborate across the organization to solve critical business challenges and support overall business strategy.