Scientist 2, Data Science

Role Overview

The Data Science team within our Operations functionbuilds analytics and machine learning solutions that develop advanced analytics tools and machine learning models to solve complex business problems using mathematics, statistics, and machine learning methods, and take ideas from concept through data preparation, model building, application development, and system integration. You will work hands-on across advanced analytics and machine learning model development, data preparation, and application development, using tools and environments including Python, GitHub, Linux, Docker, and machine learning algorithms.

Essential Duties and Responsibilities

• Automate data extraction, cleaning, processing, feature engineering, and exploratory analysis using Python, R, and SQL across structured and unstructured operational datasets.
• Develop advanced machine learning and deep learning models to support operational excellence, predict outcomes of interest, and identify patterns across enterprise datasets
• Build and deploy end-to-end analytics applications, covering data preparation, model development, and system integration
• Visualize analytical findings and model outputs using enterprise BI tools such as Tableau, Power BI, or similar platforms to create intelligent dashboards that support operational decision-making.
• Validate and monitor deployed models in production, tracking performance drift and retraining as needed to maintain accuracy and reliability.
• Document model methodologies, data pipelines, and application architecture using version-controlled repositories to support reproducibility and team collaboration.
• Translate analytical findings into clear, actionable insights and present results to engineering and operations partners across functions.
• Leverage AI-assisted tools to accelerate workflows, while critically evaluating outputs before use.

Required Qualifications

• Master's degree in Data Science, AI, Statistics, Computer Science, Mathematics, or a related quantitative field.
• Fresh graduates are welcome to apply.
• Proficiency in Python and SQL for data manipulation, pipeline development, and model implementation.
• Hands-on experience with machine learning algorithms spanning regression, classification, clustering, and deep learning methods.
• Familiarity with software development tools and environments including version control, Linux-based systems, and containerization tools
• Experience building data visualizations or dashboards using enterprise BI tools (e.g., Tableau or Power BI)

Preferred Qualifications

• Experience deploying machine learning models to production environments using MLOps practices or containerized workflows.
• Familiarity with cloud platforms such as AWS, GCP, or Azure for data storage, model training, or deployment.
• Exposure to generative AI or large language model (LLM) applications in an analytical or engineering context.
• Knowledge of statistical modeling techniques including time series analysis, anomaly detection, or survival analysis
• Experience with R for statistical analysis and modeling.

Skills

• Ability to work across the full data science lifecycle, from messy raw data to a deployed, monitored model, without losing sight of the business problem being solved.
• Strong statistical reasoning and the ability to choose the right modeling approach for the problem at hand, not just the most complex one.
• Clear communicator who can translate model outputs and analytical findings into conclusions that engineering and operations partners can act on, reflecting a commitment to enabling customer and stakeholder success
• Collaborative working style with the ability to contribute meaningfully within a team of engineers, data scientists, and business partners while driving progress on independent workstreams.
• Disciplined approach to code quality, documentation, and reproducibility, treating version control and peer review as standard practice rather than optional steps.
• Curiosity-driven problem solver who brings both rigor and creativity to analytical challenges, identifying improvement opportunities large and small.