{"id":3133458,"url":"https://alion.io/job/lenovo-ai-synthetic-data-engineer","title":"AI & Synthetic Data Engineer","company":{"id":15044,"name":"Lenovo","domain":"lenovo.com","url":"https://alion.io/company/lenovo","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Avature","truth_index":{"grade":"A","score":100,"open_postings":5,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":9,"computed_at":"2026-10-25T05:49:26Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Beijing, China"],"countries":["CN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Polars","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Diffusion Models","optional":true},{"name":"JAX","optional":true},{"name":"PyTorch","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-23T04:44:50Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-25T06:42:15Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Role Overview\nWe 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.\nJob Responsibilities：\nResearch, design, and implement synthetic data generation and evaluation solutions for different business and technical scenarios.\nProcess, clean, transform, and analyze structured and unstructured datasets.\nEvaluate the quality, representativeness, privacy, and utility of synthetic data using appropriate statistical and machine-learning methods.\nDevelop prototypes and proof-of-concept solutions using Python and modern AI-assisted coding tools.\nApply machine learning, deep learning, statistical analysis, or reinforcement learning techniques where appropriate.\nCollaborate with technical and business stakeholders to translate requirements into practical data and AI solutions.\nDocument technical approaches, experimental results, assumptions, and recommendations.\nStay current with emerging technologies and best practices in synthetic data, generative AI, privacy-preserving technologies, and data science.\nJob Requirements：\nStrong logical and computational thinking, with the ability to break down complex problems into structured and executable solutions.\nBasic 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.\nStrong mathematical foundation, particularly in areas such as probability, statistics, linear algebra, optimization, or numerical methods.\nUnderstanding of synthetic data concepts, methodologies, use cases, and evaluation approaches.\nHands-on experience with data-processing and scientific-computing libraries such as:\npandas\nNumPy\nSciPy\nPolars\nscikit-learn\nAbility to independently conduct technical research, experiments, and proof-of-concept development.\nStrong analytical and problem-solving skills.\nPreferred Qualifications\nProficiency in Python is strongly preferred.\nPractical experience in one or more of the following areas:\nMachine learning\nStatistical modeling and analysis\nDeep learning\nReinforcement learning\nGenerative AI\nData privacy and privacy-preserving computation\nExperience with deep-learning frameworks such as PyTorch, TensorFlow, or JAX.\nFamiliarity with synthetic data generation methods, including statistical simulation, generative models, GANs, VAEs, diffusion models, or large language models.\nExperience evaluating synthetic data across dimensions such as statistical fidelity, downstream utility, bias, privacy risk, and compliance.\nExperience working with large-scale datasets, data pipelines, or cloud-based development environments.\nStrong communication skills and the ability to explain complex technical concepts to non-technical 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