Data Scientist

Job Description

In this role, you will manage and execute statistical modeling and optimization efforts supporting scientific data cleansing and testing initiatives. You will work closely with scientific stakeholders across North America and global partners, translating scientific questions into structured data science approaches. Your day-to-day work will involve acquiring and profiling data, performing exploratory data analysis, cleaning and correcting data quality issues, and applying appropriate modeling techniques—often with small, high-value datasets where rigor and creativity are essential. You will evaluate and leverage third-party platforms used for modeling and optimization, contribute to data quality and governance standards, and clearly communicate findings, assumptions, and limitations to both technical and non-technical stakeholders. The role requires strong independent execution, thoughtful problem framing, and disciplined, well-documented coding practices that enable others to understand and build upon your work.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to View email address on click.appcast.io learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

Skills and Requirements

Applied Statistical Modeling & Optimization Experience: Strong grounding in statistical theory and modeling techniques, with hands-on experience selecting, applying, and evaluating modeling approaches to drive decision-making and outcomes.

Experience Working with Small, High-Value Datasets: Demonstrated experience working with limited datasets (e.g., 15–100 data points), including applying techniques to prevent overfitting and maximize learning when additional data collection is costly.

Individual, End-to-End Project Ownership: Proven ability to independently own and execute data science work—from messy data ingestion and cleansing through modeling, validation, and delivery—without reliance on large project teams.

Data Cleaning & Exploratory Data Analysis Expertise: Comfort working with unstructured, messy, or incomplete data, applying creativity and rigor in data cleansing, profiling, and exploratory analysis to uncover usable insights.

Scripting in Python and/or R with Strong Coding Hygiene: Practical experience scripting in Python and/or R, with a methodical approach to testing, debugging, documentation, and code readability (clear structure, comments, and logical organization). Experience with Third-Party Modeling or Optimization Platforms: Exposure to externally executed platforms (e.g., optimization engines such as Bayesian or global optimization methods), with the ability to evaluate techniques rather than build everything from scratch.

Comfort Translating Scientific Concepts into Data Science Requirements: Experience working with scientists or technical stakeholders, interpreting experimental or scientific concepts, and translating them into data models and analytical approaches.

Background in Applied or Constrained-Data Environments: Experience from academia, government, applied engineering, or regulated industries where data access is limited and modeling rigor is essential.

Data Governance & Quality Mindset: Familiarity with data quality management, data definitions, profiling, reference/master data concepts, and maintaining analytical integrity across datasets.

Exposure to Version Control & Collaborative Ways of Working: Working knowledge of Git/version control and an interest in improving collaboration, code sharing, and standardized ways of working within a data science team.