Senior Data Scientist
Description
The Business Data Science and AI team works with business teams across the organization to identify, assess, and solve real business problems by analyzing all available data, including enterprise, connected product, and publicly available datasets, to create and discover meaningful insights.
The Senior Data Scientist leads the design and delivery of Data Science and AI solutions across NOV’s global operations, mentors junior talent, and translates insights into measurable business outcomes.
As a Senior Data Scientist, you will:
Partner with business leaders to identify high-value opportunities, understand workflows, define requirements, and select analytical approaches across functions such as sales, manufacturing, supply chain, and finance.
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.
REQUIRED QUALIFICATIONS
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 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.
PREFERRED QUALIFICATIONS
Knowledge of sales, manufacturing operations, supply chain, ERP systems, or finance is a plus.
Experience with Databricks or Snowflake; 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 quantitative discipline preferred.