AI & Synthetic Data Engineer
Confirmed on the employer's own hiring board on Oct 23, 2026. First seen by Alion on Oct 23, 2026. Lenovo scores A on the Alion truth index.
Role Overview
We are looking for an AI & Synthetic Data Engineer with strong mathematical foundations, solid programming logic, and hands-on experience in AI-assisted development and data processing. The ideal candidate should be passionate about synthetic data and capable of applying statistical analysis, machine learning, and other advanced AI techniques to solve practical business and technical problems.
Job Responsibilities：
Research, design, and implement synthetic data generation and evaluation solutions for different business and technical scenarios.
Process, clean, transform, and analyze structured and unstructured datasets.
Evaluate the quality, representativeness, privacy, and utility of synthetic data using appropriate statistical and machine-learning methods.
Develop prototypes and proof-of-concept solutions using Python and modern AI-assisted coding tools.
Apply machine learning, deep learning, statistical analysis, or reinforcement learning techniques where appropriate.
Collaborate with technical and business stakeholders to translate requirements into practical data and AI solutions.
Document technical approaches, experimental results, assumptions, and recommendations.
Stay current with emerging technologies and best practices in synthetic data, generative AI, privacy-preserving technologies, and data science.
Job Requirements：
Strong logical and computational thinking, with the ability to break down complex problems into structured and executable solutions.
Basic programming skills and experience with AI-assisted development or “vibe coding”, including the effective use of AI coding tools to build, test, debug, and iterate on prototypes.
Strong mathematical foundation, particularly in areas such as probability, statistics, linear algebra, optimization, or numerical methods.
Understanding of synthetic data concepts, methodologies, use cases, and evaluation approaches.
Hands-on experience with data-processing and scientific-computing libraries such as:
pandas
NumPy
SciPy
Polars
scikit-learn
Ability to independently conduct technical research, experiments, and proof-of-concept development.
Strong analytical and problem-solving skills.
Preferred Qualifications
Proficiency in Python is strongly preferred.
Practical experience in one or more of the following areas:
Machine learning
Statistical modeling and analysis
Deep learning
Reinforcement learning
Generative AI
Data privacy and privacy-preserving computation
Experience with deep-learning frameworks such as PyTorch, TensorFlow, or JAX.
Familiarity with synthetic data generation methods, including statistical simulation, generative models, GANs, VAEs, diffusion models, or large language models.
Experience evaluating synthetic data across dimensions such as statistical fidelity, downstream utility, bias, privacy risk, and compliance.
Experience working with large-scale datasets, data pipelines, or cloud-based development environments.
Strong communication skills and the ability to explain complex technical concepts to non-technical stakeholders.