Senior Scientist Jobs

About The Position

Requirements

• Degree in a quantitative discipline such as Computer Science, Engineering, Statistics, or Mathematics, or equivalent experience.
• 3 to 5 years of data science experience, including delivering high-impact projects from problem definition through production and demonstrating business results; equivalent experience is considered.
• Strong communication skills, including explaining technical work and influencing executive stakeholders.
• Advanced SQL and Python skills.
• Applied machine learning expertise in supervised and unsupervised learning, deep learning, feature engineering, statistical modeling, validation, and optimization, with evaluation against business outcomes.
• Experience deploying and supporting enterprise-scale data science or AI solutions in AWS or Azure cloud environments.
• Working knowledge of MLOps and software engineering, including version control, testing, CI/CD, experiment tracking, model versioning, deployment, and monitoring.

Nice To Haves

• Knowledge of sales, manufacturing operations, supply chain, ERP systems, or finance.
• Experience with Databricks or Snowflake; experience with both is a plus.
• Strong PySpark skills for distributed processing.
• Practical knowledge of agentic AI, including tool/function calling, retrieval-augmented generation, orchestration, guardrails, observability, and evaluation; ownership of at least one solution from architecture through deployment or operations.
• Master’s or PhD in a quantitative discipline.

Responsibilities

• Partner with business leaders to identify high-value opportunities, understand workflows, define requirements, and select analytical approaches across sales, manufacturing, supply chain, finance, and other functions.
• Collaborate with domain experts, business analysts, data engineers, software engineers, and integration teams.
• Lead cross-business-unit and global data science initiatives, defining project scope, timelines, and deliverables with business leaders.
• Develop and validate machine learning models, from data preparation and feature engineering through algorithm selection and optimization.
• Deploy and maintain production-ready models and AI applications, including evaluation, monitoring, alerting, versioning, and retraining.
• Design and evaluate agentic AI solutions, including tool-using workflows and retrieval-augmented generation when appropriate.
• Establish reusable analytical and engineering practices for scalable, reliable solutions.
• Apply data governance, privacy, and security standards.
• Mentor interns, junior data scientists, and analysts.
• Communicate findings and actionable recommendations through clear visualizations to technical, business, and executive audiences.