Productivity Data Science, Analytics & AI Specialist

Baker Hughes•Skaneateles, NY

1d•$87,870 - $163,120

About The Position

This role sits within the SP&S Integrated Supply Chain Operations Excellence team and focuses on using advanced analytics, machine learning, and AI to improve decision-making, reduce cost and value leakage, and support stronger delivery performance. The specialist develops applied data science and AI solutions for operational and financial challenges, working across functions to identify high-value use cases, build trusted analytical tools, and support responsible AI adoption. The role applies statistical and machine learning techniques to operational, business, and financial data across the project lifecycle. It also develops AI-powered solutions using large language models for document analysis, summarization, insight extraction, and workflow acceleration; predictive and diagnostic models for margin erosion, cost leakage, delivery risk, and project performance; and Power BI dashboards and semantic models combining descriptive analytics with predictive and data science outputs. The specialist translates analytical and AI outputs into actionable insights and builds maintainable code and repeatable data pipelines that integrate into business workflows.

Requirements

5–8 years of overall experience.
Strong applied data science experience, including statistical modelling, predictive analytics, and machine learning applied to real business problems.
Proficiency in Python, SQL, and C#, with experience in data transformation, modelling, automation, and analytical pipeline development.
Strong Power BI capability, including semantic modelling and DAX, and the ability to communicate insights through visual reporting.
Practical experience with generative AI and LLM-based applications, including prompt design, workflow integration, or custom AI tools.
Ability to balance experimentation with pragmatism, use evidence to guide decisions, and focus on value over novelty.
Comfort working with imperfect or evolving data and helping improve data quality and governance.
Effective communication with technical and non-technical stakeholders and collaborative work across functions.

Nice To Haves

Experience in project-based, engineering, manufacturing, industrial, or operational environments.
Exposure to cost, margin, productivity, or performance data.
Familiarity with cloud AI or machine learning platforms such as Microsoft Azure AI Foundry, AWS Bedrock, or similar.
Understanding of data governance, responsible AI principles, and scalable delivery practices.
A curious, pragmatic, and action-driven approach; confidence working through ambiguity and discipline in turning ideas into measurable outcomes.

Responsibilities

Develop AI agents, prompt-engineered workflows, and automated solutions tailored to business-specific use cases.
Focus on business-led use cases that address slow, repetitive, fragmented, or decision-heavy processes and have a clear link to time, quality, risk, insight, or customer benefit.
Work with teams across the value stream to identify opportunities, surface dependencies, and shape practical pilots, standards, and scalable approaches.
Support responsible AI adoption by considering governance, data quality, privacy, consistency, and alignment with wider business and digital standards.
Help raise data and AI literacy within the wider team.
Apply statistical and machine learning techniques to operational, business, and financial data to identify trends, risks, and opportunities across the project lifecycle.
Design and develop AI-powered solutions using large language models for document analysis, summarization, insight extraction, and workflow acceleration.
Build predictive and diagnostic models to identify margin erosion, cost leakage, delivery risk, and leading indicators of project performance.
Design and maintain Power BI dashboards and semantic models combining descriptive analytics with predictive and data science outputs.
Translate analytical and AI outputs into clear, actionable insight for operational, project, finance, and leadership stakeholders.
Write clean, maintainable code and build repeatable data pipelines to automate preparation, analysis, and integration into business workflows.