Data Scientist, R&D

Church & Dwight•Princeton, NJ

1d•Hybrid

About The Position

Church & Dwight is seeking a Data Scientist to join its R&D Agentic Pod team, which designs, builds, deploys, and scales AI-powered products that transform how R&D teams make decisions and operate. The role combines statistical and machine learning expertise with R&D process knowledge to address challenges across consumer insights, product development, packaging, manufacturing processing, quality, and regulatory work. The Data Scientist develops predictive, prescriptive, optimization, measurement, and performance-monitoring solutions; translates complex data into practical recommendations; and embeds analytical outputs into decision-support products and AI-enabled workflows. Working with R&D, Quality, Regulatory, Consumer Insights, AI Champions, GTA partners, analytics leaders, data engineering teams, and AI Engineers, the role contributes across the solution lifecycle, from identifying opportunities and developing models through deployment, adoption, measurement, and improvement. Success is measured by analytical rigor, model quality, usefulness of recommendations, user adoption, and measurable commercial impact. The position is part of the Global R&D Innovation & AI organization and reports to the Lead, R&D Agentic Pod. It is hybrid and based in Princeton, New Jersey, with in-person attendance on Tuesdays, Wednesdays, and Thursdays and remote work options on Mondays and Fridays.

Requirements

Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Econometrics, Analytics, Operations Research, or a related quantitative discipline.
At least 3 years of experience in data science, advanced analytics, machine learning, decision science, or a related field.
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, and model validation.
Ability to translate analysis into recommendations for non-technical stakeholders.
Knowledge of probability distributions, hypothesis testing, statistical inference, experimental design, causal measurement, supervised and unsupervised learning, forecasting, and optimization.
Skills in data visualization, storytelling, and translating technical findings into practical recommendations and measurable outcomes.
Action-oriented, data-driven problem solver focused on measurable outcomes; strategic systems thinker with technical, business, and product acumen; collaborative, curious, and accountable, with the ability to work effectively in ambiguity and influence without authority.

Nice To Haves

Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Econometrics, Analytics, Operations Research, or a related quantitative discipline.
Experience with R&D, Quality, or Regulatory datasets and Consumer Packaged Goods (CPG).
Knowledge of R&D applications such as consumer insights, scientific knowledge discovery, formulation optimization, product performance modeling, complaint signal detection, regulatory intelligence, and manufacturing quality and process optimization.
Experience or knowledge in analytical product delivery, documentation, model monitoring, data quality and governance fundamentals, responsible AI, and reliable decision-support solutions.

Responsibilities

Partner with R&D, Quality, Regulatory, and Consumer Insights teams to frame business questions, define decision criteria, and identify, evaluate, and prioritize analytics opportunities.
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 recommendations, scenarios, and decision tools.
Create proofs of concept and minimum viable analytical products, and 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.