leading and delivering successful data science projects. Proficiency in programming languages such as Python, R, or Scala. Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn. Strong knowledge of statistical modelling, data mining, and data visualization techniques. Experience with big data technologies (e.g., Hadoop, Spark) and more »
discipline eg. Statistics, Mathematics, Physics, Machine Learning Deep expertise in Python (production-level) and SQL Proficiency in machine learning libraries (eg. Pandas, scikit-learn, TensorFlow) and experience with MLOps frameworks for model deployment Exceptional communication skills, able to engage confidently with non-technical stakeholders Experience resolving operational or customer more »
with a strong focus on machine learning and time series forecasting. Expertise in Python and its data science libraries (e.g., Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch). Solid understanding of ML and data pipeline architectures and best practices. Experience with big data technologies and distributed computing (e.g., Spark, Hadoop more »
data manipulation Prior work experience with analytical/statistical data analysis tools such as R and Python and deep learning libraries such as PyTorch, TensorFlow, Keras Familiarity with data visualization and dimensionality reduction algorithms Ability to develop, benchmark and apply predictive algorithms to generate hypotheses Comfortable working in cloud more »
Machine Learning, or a related field. Experience in AI/Machine Learning research and development. Proficiency in Python. Experience with popular machine learning frameworks (TensorFlow, PyTorch, scikit-learn). Experience with using NVIDIA GPUs for fine tuning AI models Strong mathematical and statistical background. Excellent problem-solving and critical more »
Medoids or similar and the skills to evaluate solution suitability e.g., Silhouette Score. Confident in the skills needed to deploy deep learning, ideally through Tensorflow, including transformer architectures. Natural language processing capabilities, including skills in sentiment analysis and using and localising large language models (transformers), ideally through the Hugging more »
of the associated long-term risks and ethical considerations. Technologies: AI/ML Frameworks and Tools: Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and more »
of the associated long-term risks and ethical considerations. Technologies: AI/ML Frameworks and Tools: Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and more »
of the associated long-term risks and ethical considerations. Technologies: AI/ML Frameworks and Tools: Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and more »
of the associated long-term risks and ethical considerations. Technologies: AI/ML Frameworks and Tools: Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and more »
of the associated long-term risks and ethical considerations. Technologies: AI/ML Frameworks and Tools: Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and more »
understanding of the associated long-term risks and ethical considerations. AI/ML Frameworks and Tools: Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and more »
understanding of the associated long-term risks and ethical considerations. AI/ML Frameworks and Tools: Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and more »
understanding of the associated long-term risks and ethical considerations. AI/ML Frameworks and Tools: Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and more »
a Data Scientist at IBM, you will work to solve business problems using leading edge and open-source tools such as Python, R, and TensorFlow, combined with IBM tools and our AI application suites. You will prepare, analyze, and understand data to deliver insight, predict emerging trends, and provide more »
legal field, balanced with a deep understanding of the associated long-term risks and ethical considerations. Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and more »
legal field, balanced with a deep understanding of the associated long-term risks and ethical considerations. Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and more »
or a related field. Strong background in deep learning, with hands-on experience in developing and implementing deep neural networks using frameworks such as TensorFlow, PyTorch, or Keras. Proficiency in programming languages such as Python and experience with data manipulation and analysis libraries (e.g., pandas, NumPy, scikit-learn). more »
and deploying machine learning models and algorithms in real-world applications. - Strong proficiency in Python programming and popular machine learning libraries/frameworks (e.g., TensorFlow, PyTorch, scikit-learn). - Deep understanding of machine learning concepts and techniques, including supervised/unsupervised learning, deep learning, reinforcement learning, etc. - Strong communication more »
Technical Skills: Proficiency in Python, R, SQL, and big data technologies such as Hadoop, Spark, or Kafka. Experience with machine learning frameworks such as TensorFlow or PyTorch. Analytical Skills: Strong problem-solving skills with the ability to derive meaningful insights from complex datasets. Communication: Excellent verbal and written communication more »
Linux, Docker, CI/CD. Has a strong opinion on their IDE/editor of choice Familiarity with modern machine learning tools, for instance TensorFlow, Keras, PyTorch or SKLearn. Commercial experience with these is not essential.Knowledge of MLOps is not essential, but some awareness of this emerging space is more »
and advanced analytics technologies, coupled with the ability to discern their feasibility and long-term impact. Expertise in popular machine learning libraries such as TensorFlow, PyTorch, and Keras. Proficient in using AI/ML platforms like Google AI Platform, AWS SageMaker, or Azure Machine Learning for model development and more »
Bedford, Bedfordshire, United Kingdom Hybrid / WFH Options
Understanding Recruitment
in PythonStrong NLP experience - NumPy, Pandas etc.Commercial experience leveraging open-source models, finetuning LLMs & RAG pipelinesExpertise in learning algorithms, neural networks and ML frameworks (TensorFlow, PyTorch etc.)MLOps experienceNice to have:Familiarity with Git or other Version Control SystemsComputer Vision Library exposureUnderstanding of Big Data Technologies (Hadoop, Spark etc more »
record of deploying models in production settings. Advanced proficiency in Python and familiarity with machine learning and deep learning frameworks (e.g. Scikit-learn, PyTorch, TensorFlow). Experience with containerization technologies (e.g., Docker, ECR) and an understanding of GPU acceleration for deep learning. Expertise in a range of machine learning more »
