Senior Data Scientist (PhD)

About the role

ATP is looking for a Senior Data Scientist with a PhD to lead high-impact projects that connect advanced research with production-ready AI applications. Candidates with specialist backgrounds in Natural Language Processing, Computer Vision, Robotics or related AI fields are welcome. The role covers the full development lifecycle, from problem formulation and experimentation to deployment and monitoring.

Key responsibilities

• Lead end-to-end machine learning projects, from problem definition and data preparation to deployment and production monitoring.
• Translate business requirements into research questions, technical specifications and measurable success criteria.
• Develop models and data pipelines aligned with the specialization and project needs.
• Compare methods against established baselines and validate improvements through controlled experiments.
• Conduct error analysis and ablation studies to understand model behaviour.
• Collaborate with domain specialists and software engineers to integrate models into products and systems.
• Build reproducible workflows using experiment tracking, data and model versioning, automated testing and documentation.
• Optimize models for production and monitor their performance and reliability.
• Review scientific literature, reproduce promising approaches and mentor junior team members.

Required profile

• PhD in Computer Science, AI, Robotics, Electrical Engineering, Statistics, Mathematics or a related quantitative field, with research relevant to machine learning or AI.
• 5+ years of hands-on experience in data science and machine learning on real-world applications.
• Research or applied background in NLP, computer vision, robotics, generative AI or statistical learning.
• Experience preparing and analysing datasets such as text, images, video, time-series or sensor data.
• Ability to design reproducible experiments, set baselines and perform systematic error analysis.
• Understanding of data leakage, overfitting, distribution shift and robust validation; experience balancing model quality, latency, cost and deployment constraints.
• Strong collaboration and communication skills, with English fluency.

Required skills

• Strong Python, with PyTorch or TensorFlow, and Pandas, NumPy and scikit-learn.
• Statistical modelling, hypothesis testing, experimental design and model evaluation.
• Nice to have: peer-reviewed publications, experience turning doctoral research into industry applications, multimodal learning, model optimization, edge or cloud ML platforms.