We are seeking an R&D Senior Data Scientist to drive innovation at the intersection of research and applied machine learning.
In this role, you’ll own the end-to-end lifecycle of advanced ML/DL systems—from research and data strategy to deployment—while shaping our technical roadmap and mentoring peers.
If you are passionate about transforming cutting-edge research into production-ready solutions, this is your opportunity.
Key Responsibilities:
• End-to-End Model Development: Architect, design, and implement novel ML/DL systems from research to deployment, supporting ML and software engineering teams.
• Innovation and Research: Proactively explore state-of-the-art algorithms, frameworks, and tools, and translate academic research into practical, impactful applications.
• Influence Technical Strategy: Shape the AI/ML roadmap through key decisions on model selection, data representation, and MLOps tooling.
• Mentor and Evangelize: Share knowledge, mentor team members, and promote best practices to elevate the company’s AI/ML capabilities.
Mandatory Skills:
• 5+ years of applied AI/ML research or senior data science experience.
• Expert-level Python skills with strong knowledge of the data science ecosystem (Pandas, NumPy, Scikit-learn, Jupyter).
• Deep hands-on expertise with PyTorch or TensorFlow.
• Proficiency in at least one OOP language (C#, C++, Java).
• Strong applied knowledge of statistics, probability, and experimental design.
• Solid understanding of ML algorithms (regression, classification, clustering, dimensionality reduction) and DL architectures (CNNs, RNNs, Transformers, Diffusion, Generative AI).
• Strong problem-solving skills for tackling ambiguous and complex research challenges.
• Comfortable working in international teams.
• Proactive self-starter with excellent organizational and communication skills.
• Fluent in English, with curiosity and a continuous learning mindset.
Nice to Have
• Background in Computer Science/Engineering.
• Familiarity with Agile (Scrum) methodologies.
• Understanding of cloud concepts and hands-on experience with Azure AI Foundry, Google Vertex AI, or AWS SageMaker.
• Knowledge of low-level ML programming (CUDA, OpenCL, ROCm).
• Experience with the full ML lifecycle, including version control (Git, DVC) and MLOps principles.
• Familiarity with data engineering tools and orchestrators (Apache Spark, Airflow, Kubeflow Pipelines).
• Public speaking experience and/or contributions to the AI community.
• A public portfolio (GitHub, Kaggle), academic publications, or experience presenting at conferences/meetups.
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