Covetrus

Consumer Data Scientist

Covetrus

full-time

Posted on:

Location Type: Hybrid

Location: Portland • Maine, Ohio • 🇺🇸 United States

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Salary

💰 $78,600 - $112,300 per year

Job Level

Mid-LevelSenior

Tech Stack

PythonSQLTableau

About the role

  • Drive and support the development, validation, and deployment of predictive and machine learning models 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.

Requirements

  • Bachelor’s degree in Statistics, Mathematics, Economics, Computer Science, Engineering, or a related quantitative field required
  • Advanced degree (Master’s or PhD) in a quantitative discipline a plus
  • 3+ years of experience in applied data science, machine learning, or advanced analytics in a consumer, digital, or eCommerce environment.
  • 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.
  • 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).
Benefits
  • 401k savings & company match
  • Paid time off
  • Paid holidays
  • Maternity leave
  • Parental leave
  • Military leave
  • Other leaves of absence
  • Health, dental, and vision benefits
  • Health savings accounts
  • Flexible spending accounts
  • Life & disability benefits
  • Identity theft protection
  • Pet insurance

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
predictive modelingmachine learningregressionregularizationtree-based methodsclassificationclusteringtime-series forecastinguplift modelingdata analysis
Soft skills
analytical skillsquantitative skillscommunicationstorytellingmentoringcollaborationproblem-solvingstrategic thinkinginsight generationadaptability