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Our unique, informal culture champions courage, determination and collaboration. Knowing we have an open and supportive team means each of us has the freedom to take responsibility and ownership. We have a shared passion to work hard, innovate and push boundaries.

United by the belief that when we strive for growth, anything is possible. While we might not be the largest company in our industry, we believe we can have the biggest impact because: Together We Have the Power to Win.

Job Summary 

Church & Dwight is seeking a Data Scientist to join our R&D Agentic Pod team, which is responsible for designing, building, deploying, and scaling AI-powered products that transform how R&D teams make decisions and operate. The Data Scientist, R&D Agentic Pod combines rigorous statistical and machine learning expertise with strong research & development process acumen to solve high-value challenges across consumer insights, product development, packaging, manufacturing processing, quality, and regulatory.

About the Role 

The Data Scientist, R&D will develop predictive, prescriptive, optimization, measurement and performance monitoring solutions; translate complex data into practical recommendations; and help embed analytical outputs into decision-support products and AI-enabled workflows.

Working closely with R&D, Quality, & Regulatory stakeholders, AI Champions, GTA partners, analytics leaders, data engineering teams, and AI Engineers, this role contributes across the full solution lifecycle, from opportunity identification and model development through deployment, adoption, measurement, and continuous improvement.

Success in this role will be measured by analytical rigor, model quality, usefulness of recommendations, user adoption, and measurable commercial impact.

The role sits within the Global Technology and Analytics (GTA) R&D Innovation & AI organization, reporting to the Lead, R&D Agentic Pod. 

Work Environment

Hybrid: This is a hybrid role based in Princeton, NJ, and requires in- person attendance on Tuesdays, Wednesdays, and Thursdays, with remote work options on Mondays and Fridays.

Role Accountabilities and Responsibilities

• Partner with R&D, Quality, Regulatory, and Consumer Insights teams to frame business questions, define decision criteria, identify high-value analytics opportunities, evaluate and prioritize opportunities based on business value, analytical feasibility, data readiness, scalability, risk, and strategic alignment.
• Develop predictive, prescriptive, segmentation, recommendation, propensity, forecasting, causal, experimental, and optimization solutions using structured and unstructured data.
• Build, validate, maintain, and monitor machine learning models in Python using scikit-learn, XGBoost, or comparable frameworks.
• Define features, assumptions, validation methods, performance metrics, and monitoring plans; assess bias, stability, explainability, failure modes, and business relevance.
• Design test-and-learn and measurement frameworks to quantify incremental impact and translate findings into clear recommendations, scenarios, and decision tools.
• Create proofs of concept and minimum viable analytical products; embed models and measurement logic into dashboards, applications, agents, and other decision-support experiences.
• Partner with AI Engineers, GTA, Enterprise Data Platform, and data engineering teams to productionize solutions and enable reliable, reusable, analysis-ready datasets; identify data quality, coverage, and availability gaps and recommend practical remediation approaches or viable analytical alternatives.
• Document methodologies, assumptions, limitations, data lineage, and intended use; ensure compliance with data governance, privacy, security, and responsible AI requirements.
• Drive adoption through stakeholder communication, training, reusable best practices, user feedback, and continuous improvement.
  

 Skills and Competencies

• Action-oriented, data-driven problem solver focused on measurable outcomes.
• Strategic systems thinker with strong technical, business, and product acumen.
• Collaborative, curious, and accountable; effective in ambiguity and influencing without authority.

Technical Skills

•  Statistics and Machine Learning: Probability distributions, hypothesis testing, statistical inference, linear algebra, experimental design, causal measurement, supervised and unsupervised learning, forecasting, and optimization.
• Programming and Modeling: Python, SQL, scikit-learn, XGBoost, feature engineering, model validation, explainability, and data preparation and transformation.
• Analytics and Decision Support: Data visualization, storytelling, data-driven decision-making, and translation of technical findings into practical recommendations and measurable outcomes.
• R&D Applications: Consumer insights, scientific knowledge discovery, formulation optimization, product performance modeling, complaint signal detection, regulatory intelligence, and manufacturing quality and process optimization.
• Analytical Product Delivery and Governance: Analytical product thinking, documentation, model monitoring, data quality and governance fundamentals, responsible AI, and reliable decision-support solutions.

Education and Experience

Required Qualifications:

• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Econometrics, Analytics, Operations Research, or a related quantitative discipline required.
• 3+ years of experience in data science, advanced analytics, machine learning, decision science, or a related field required.
• Demonstrated experience applying statistics, linear algebra, and machine learning to business problems.
• Hands-on experience building and evaluating Python models using scikit-learn, XGBoost, or comparable frameworks.
• Experience with SQL, large structured datasets, data preparation, feature engineering, model validation, and translating analysis into recommendations for non-technical stakeholders.

Preferred Qualifications:

• Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Econometrics, Analytics, Operations Research, or a related quantitative discipline preferred
• Experience with R&D, Quality, or Regulatory datasets and Consumer Packaged Goods (CPG).

Compensation and Benefits

The U.S. base salary range for this full-time position is $71,000– $101,100. This position is also eligible for a bonus. 

As a Church & Dwight US employee, you (and eligible dependents, as applicable) will have access to medical, dental, vision, basic life insurance, paid vacation and sick time, and Paid Parental Leave. U.S. employees are entitled to paid holidays, floating holidays, and vacation days starting in their first year of employment depending on hire date. You are also able to participate in our 401k retirement plan (with company match and profit-sharing) and Discounted Employee Stock Purchase Plan.   

The actual base pay offered to the successful candidate will be based on multiple factors, including, but not limited to, job-related knowledge/skills, experience, business needs, geographical location, and internal pay parity.  Compensation decisions are dependent upon the facts and circumstances of each position and candidate.

#LI-Hybrid

Church & Dwight is proud to be an Equal Opportunity Employer/Veterans/Individuals with Disabilities.

For more information on our company, our brands and our culture visit us at http://www.churchdwight.com/