Senior Data Scientist

急速に変化するテクノロジーとコネクティビティの分野で、成功への原動力となる課題解決に挑戦しませんか？あなたの発想力、熱意、そして問題解決力で、エレクトロニクスのさらなる飛躍を形にしましょう。

Qnityは、先端エレクトロニクスやハイテク産業向けの材料・ソリューションを提供するグローバルリーダーです。しかし、私たちの強みは製品だけではありません。新しい可能性に挑戦し続け、常にチャレンジを楽しむ結束力のある仲間が集まっています。すべてのメンバーが、最先端技術の実現に貢献しています。

Qnityは、未来を見据えて挑戦する人、既成概念を打ち破る人、そして多様な視点を歓迎します。なぜなら、私たちは世界の進歩を加速させる重要な役割を担っているからです。Qnityで、あなたのキャリアをスタートさせる、または次のステージへ進めるチャンスを見つけてください。

Qnity Electronics is accepting applications for a position as a Data Scientist. This position will be at Qnity’s Cleveland site, Ohio USA. This is an exciting opportunity to join a growing team that plays a vital and dynamic role in the company’s R&D department.

Qnity Electronics is a premier technology provider across the semiconductor value chain, empowering AI, high performance computing, and advanced connectivity. In this role, the Data Scientist will collaborate with scientists within Qnity, who are engaged in a variety of research and development projects.

A strong candidate will have expertise in data science techniques such as data cleaning, statistical modeling, machine learning, deep learning, computer vision, generative AI, and large language models (LLMs). The applicant should demonstrate strong coding proficiency in Python or R, with the ability to prepare and analyze complex datasets, develop predictive and prescriptive models, build ML/DL pipelines, create compelling visualizations and reports, and evaluate advanced AI models including transformer-based architectures and multimodal systems.

Beyond technical expertise, the role requires a highly organized problem solver who can apply structured analytical thinking to complex scientific and business challenges, effectively communicate technical concepts and results to stakeholders, and collaborate closely with R&D, data engineering, and domain experts to develop, deploy, and scale AI-driven solutions.

• PhD or MS + 3 years of experience in Data Science, Materials Science, Chemistry, Engineering, or a related technical field.

• Strong programming skills in Python, including data preparation, statistical analysis, feature engineering, model development, evaluation, and visualization.

• Demonstrated expertise in statistical modeling, machine learning, and deep learning across regression, predictive modeling, experimental design, hypothesis testing, supervised and unsupervised learning, classification, clustering, neural networks, and transformer-based architectures.

• Strong hands-on experience with Computer Vision, including image classification, object detection, segmentation, anomaly detection, and image-based predictive modeling.

• Experience with Generative AI and Large Language Models (LLMs), with expertise in prompt engineering, Retrieval-Augmented Generation (RAG), AI agents/copilots, and knowledge extraction applications.

• Hands-on experience developing, validating, and deploying AI/ML solutions to solve scientific, engineering, or business problems.

• Experience with modern AI frameworks such as PyTorch, Tensor Flow, Scikit-learn, OpenCV, YOLO, or…