Associate Data Scientist

American Chemical Society•Boston, MA

About The Position

The Associate Data Scientist, IT, Data & Architecture will apply foundational data science and analytical techniques to drive insights that inform research, business strategy, and organizational decision-making. Working under the guidance of senior data scientists and data engineers, this role contributes to the development of data models, dashboards, and analysis that enhances understanding of program performance, and population health trends. The Associate Data Scientist will work with diverse data sources—including healthcare, demographic, behavioral, and operational datasets—to support projects across research analytics, and business analytics. The ideal candidate is detail-oriented, intellectually curious, and eager to learn modern data tools and methods while growing into an independent, high-impact contributor. This position offers an opportunity to build technical, analytical, and strategic expertise in a collaborative, mission-driven environment that values innovation, scientific rigor, and data ethics.

Requirements

Minimum Degree Required: Bachelor's Degree in Computer Science, Engineering, Computational Biology, Econometric or equivalent experience.
1-3 years of relevant experience.
Proven experience as a data scientist from professional experience and/or formal education.
Advanced modeling expertise; Proven experience developing and validating statistical, econometric, and machine learning models for predictive or descriptive applications.
Technical proficiency in analytical programming; Expertise in Python (pandas, scikit-learn, NumPy, statsmodels, PyTorch, TensorFlow) or equivalent R packages, strong working knowledge of SQL.
Strong quantitative and analytical foundation; Deep understanding of statistical inference, experimental design, and model evaluation metrics.
Experience with complex data domains; Skilled in analyzing high-dimensional or unstructured data, such as images, text, or genomics data.
Effective communicator and storyteller; Able to translate complex analytical results into clear, actionable insights for diverse audiences.
Collaborative and business-focused mindset; Skilled at partnering with cross-functional teams to understand organizational challenges and design data-driven solutions.
Entrepreneurial drive and intellectual curiosity; Self-directed problem solver who proactively identifies opportunities for innovation and impact.

Nice To Haves

Strong proficiency in statistical modeling, machine learning, and predictive analytics using Python (pandas, scikit-learn, TensorFlow, PyTorch) or R, with experience developing and evaluating models on large, complex datasets.
Hands-on experience with SQL, data wrangling, and feature engineering, and familiarity with cloud-based data ecosystems such as Azure, Snowflake, or Databricks for scalable data processing.
Working knowledge of MLOps practices, including model versioning, experiment tracking (MLflow, DVC), and deployment workflows; experience with Git-based collaboration.
Ability to communicate and visualize results effectively using tools such as Power BI, Tableau, matplotlib, or Plotly, translating complex analyses into actionable insights for business and research audiences.

Responsibilities

Design, test, and deploy predictive and descriptive models to advance organizational strategy and outcomes, using Python, R, and related frameworks (e.g., Scikit-learn, TensorFlow, PyTorch).
Formulate and validate hypotheses through rigorous statistical testing, ensuring models are grounded in scientific and business relevance.
Apply advanced statistical, econometric, and machine learning methods to structured and unstructured datasets, including dense domains such as images or genomics.
Translate technical results into clear, actionable insights for both technical and non-technical stakeholders through effective storytelling and visualization.
Engage cross-functional teams to understand organizational challenges, frame analytical questions, and develop innovative, data-driven solutions that align with business objectives.