Data Scientist

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Lenovo is a US$69 billion revenue global technology powerhouse, ranked #196 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).

This transformation together with Lenovo’s world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. To find out more visit www.lenovo.com, and read about the latest news via our StoryHub.

Please note that the team has moved to a new location. The office is now located at 121 Menachem Begin Road, Tel Aviv, on the 61st floor of the POINT office complex. In accordance with our office policy, you will be required to visit the site at least three times a week.

Lenovo Digital Trust Lab seeks for a hands-on and innovative Data Scientist to join our cutting-edge research team. This role will directly support our Hybrid AI initiative as well as our Device, Infrastructure, and Services security programs, focusing on advancing security, privacy, and trust through innovative research and applied AI.

You will work across disciplines and play a key role in building the next-generation AI Security platform using effective, AI-driven defensestranslating theoretical ideas into practical, real-world impact.

Job Responsibilities

• Own the full ML lifecycle: data acquisition, labeling strategies (manual, weak supervision, active/semi‑supervised), EDA, feature engineering, model training, evaluation, and deployment.
• Modeling across paradigms: build supervised classifiers, unsupervised anomaly detectors, ensembles, and semi-supervised approaches tailored to imbalanced, noisy, and streaming security data.
• Performance efficiency: optimize models for strict resource budgets (CPU, memory, disk/IO) and low-latency inference; apply techniques like pruning, quantization, distillation, feature hashing, and batching.
• Maintain awareness of the latest research and technologies in security, AI, and trust, and evaluate their relevance for our innovation pipeline.
  
  

• Education: BSc/MSc in Computer Science, Electrical Engineering, Data Science, Mathematics, or related field
• 4 years of hands-on experience building and shipping ML models.
• Core ML skills: solid grasp of supervised/unsupervised learning, evaluation for imbalanced data, feature engineering, cross‑validation, and error analysis.
• Programming data: proficiency in Python, SQL; strong with pandas, NumPy, scikit‑learn; experience with at least one deep learning framework (PyTorch or TensorFlow).
• Systems performance mindset: demonstrated ability to design models under resource constraints (CPU/memory/IO) and latency budgets; comfortable profiling and optimizing code and inference paths.
• Data handling: experience building robust data pipelines; ability to work with messy, high‑volume logs and streaming data; strong data quality and validation practices.
• Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.
  
  

• BI-oriented visualization: ability to design clear, actionable graphs, charts, heatmaps, time-series views, and dashboards for stakeholders; hands-on with Power BI/Tableau/Kibana or other visualization tools
• Security product experience: hands-on work in EDR/XDR/SIEM/NDR/IDS/IPS, UEBA, malware detection, threat scoring, or incident response analytics.
• Privacy robustness: knowledge of privacy-preserving techniques (e.g., differential privacy, federated learning) and adversarial ML.
  
  

• Opportunities for career advancement and personal development
• Access to a diverse range of training programs
• Performance-based rewards that celebrate your achievements
• Flexibility with a hybrid work model (3:2)