advanced predictive modeling, optimization, scenario analysis, and statistical methodologies. Strong grasp of supervised/unsupervised methods, evaluation metrics, feature engineering, and model tuning. Proficiency in Python (pandas, NumPy, Scikit-learn); experience with PyTorch or TensorFlow for deep learning. Experience with API development and connecting AI systems to external platforms. Working knowledge in deep learning techniques, including CNNs, RNNs More ❯
into production. Prior experience leading projects or teams is a plus for a lead role Programming & ML Skills: Advanced programming skills in Python (including libraries such as pandas, scikit-learn, TensorFlow/PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps More ❯
into production. Prior experience leading projects or teams is a plus for a lead role Programming & ML Skills: Advanced programming skills in Python (including libraries such as pandas, scikit-learn, TensorFlow/PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps More ❯
generative models (GAN, VAE) to enhance predictive accuracy, interpretability, and automation. Engineer scalable analytical frameworks and reusable ML assets, integrating Python-based (or other) ML pipelines (TensorFlow, PyTorch, Scikit-learn, Pandas) with enterprise data platforms (Snowflake, Azure, Google Vertex AI) to standardise insight generation and model delivery. Collaborate with Data Architecture and Engineering to operationalise models through containerised More ❯
Experience: Proven ability to solve complex, real-world problems through data science and analytics. Experience coaching and reviewing work of junior team members. Strong Python skills (pandas, numpy, scikit-learn) and a solid grounding in probability and statistics. Deep knowledge of machine learning methods and their practical application. Experience managing multiple end-to-end data science projects across More ❯
personalization, natural language processing (NLP), or semantic search. Expert-level programming skills in Python, with deep, hands-on experience using data science and ML libraries such as Pandas, Scikit-learn, TensorFlow, or PyTorch. Experience with data storage technologies (e.g., SQL, NoSQL, Key-value) and their scaling characteristics. Experience with large-scale data processing technologies (e.g., Spark, Beam, Flink More ❯
hands-on experience as a Data Scientist, with exposure to productionised models. Proficiency in Python (production-level) and SQL; confident with modern ML/AI libraries such as scikit-learn, TensorFlow, or PyTorch. Familiarity with MLOps frameworks, model deployment, and cloud-based platforms (Databricks, AWS, Azure). It would be great if you had: Experience in the retail More ❯
experience. Proven experience designing and deploying MLOps pipelines (MLflow, Azure ML, Azure DevOps etc). Strong programming skills in Python and familiarity with common ML/AI libraries (scikit-learn, tensorflow, Keras etc.). Experience implementing machine learning and large language models (LLMs), encompassing deployment, monitoring, and retraining. Familiarity with software engineering guidelines: version control (e.g., Git), CI More ❯
years in AI/ML engineering with a track record of deploying AI into production at scale. Technical Skills: Strong backend Python; expertise with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.); familiarity with modern data pipeline tools (Airflow, Spark, Kafka) and workflow orchestration frameworks such as n8n or LangGraph. Applied AI: Background in enterprise applications such as chatbots More ❯
