Finance AI/ML Engineer - PhD
This role has been designed as ‘’Onsite’ with an expectation that you will primarily work from an HPE office.
Who We Are:
Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.
Job Description:
Develops and programs integrated software algorithms to structure, analyze, and leverage structured and unstructured data in business and systems applications. Applies machine learning and statistical modeling to improve forecasting, data quality, and decision accuracy. Formulates descriptive, diagnostic, predictive, and prescriptive insights and translates business requirements into code. In Finance, this means turning financial and operational data into AI-enabled insights and automation that help HPE plan, forecast, and operate more effectively.
Finance is shifting from reporting to decision intelligence. You'll apply a strong quantitative foundation to real finance problems, including forecasting, scenario planning, automation, and GenAI-enabled insights. You'll work alongside Finance leaders, data scientists, and AI/ML engineers. We hire for mathematical rigor, curiosity, and the ability to learn fast. Finance expertise is something you'll build here.
Responsibilities
Design, develop, and implement AI and machine learning models, including data pre-processing, feature engineering, algorithm selection, training, and evaluation.
Build forecasting and predictive models for revenue, cost, and demand, and support scenario and "what-if" analysis for planning.
Gather, clean, and analyze large financial and operational datasets to train and test models, using statistical methods and visualization to surface insights.
Optimize models for performance through hyperparameter tuning, experimentation, and feature selection or dimensionality reduction.
Support GenAI and automation use cases (AI assistants, agents, workflow automation) across FP&A, cost accounting, and controllership.
Partner with data scientists, software engineers, product managers, and Finance domain experts to understand requirements and integrate AI solutions into existing systems and workflows.
Keep clear documentation of methodologies, code, and results so models are auditable, explainable, and compliant with Finance controls.
Present findings and recommendations to Finance stakeholders in plain language. Maintain dashboards and BI tools (e.g., Power BI) as needed.
Participate in design reviews, stand-ups, and feedback cycles with your manager and team to ensure quality and continuous improvement.
Education and Experience Required:
PHD in Mathematics, Applied Mathematics, Statistics, or a closely related quantitative field.
Knowledge and Skills:
Proficiency in Python, R, or Java, plus SQL and databases. Familiarity with scikit-learn, TensorFlow, or PyTorch is highly beneficial.
Solid understanding of statistics, probability, linear algebra, calculus, and optimization methods.
Working knowledge of core ML concepts, including supervised and unsupervised learning, with exposure to deep learning, NLP, and time-series methods.
Experience with large datasets, data cleaning, and exploratory analysis. Experience with visualization tools (Matplotlib, Tableau, or Power BI).
Clear written and verbal communication, with the ability to explain complex concepts to non-technical audiences.
Nice to have
Exposure to GenAI/LLM…