methods, and predictive modeling Experience with NLP techniques for text analysis, classification, and information extraction Knowledge of deep learning frameworks such as PyTorch or TensorFlow Experience with data visualization tools (Matplotlib, Seaborn, Plotly, or similar) Strong analytical mindset with a focus on solving real-world problems Excellent communication skills More ❯
methods, and predictive modeling Experience with NLP techniques for text analysis, classification, and information extraction Knowledge of deep learning frameworks such as PyTorch or TensorFlow Experience with data visualization tools (Matplotlib, Seaborn, Plotly, or similar) Strong analytical mindset with a focus on solving real-world problems Excellent communication skills More ❯
of a wide range of ML algorithms. Understanding of MLOps practices for managing and monitoring models in production. Experience with deep learning frameworks (e.g., TensorFlow, PyTorch). Proficiency in programming languages such as Python, SQL, and R. Knowledge of SQL and NoSQL databases for data storage and retrieval. Experience More ❯
learning, and neural networks. Strong programming skills in languages such as Python, R, or Java Familiarity with AI libraries, frameworks, and tools such as TensorFlow, PyTorch, or Keras. Proven understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms. Solid understanding More ❯
learning, and neural networks. Strong programming skills in languages such as Python, R, or Java Familiarity with AI libraries, frameworks, and tools such as TensorFlow, PyTorch, or Keras. Proven understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms. Solid understanding More ❯
a production environment, MLOps and model integration into larger scale applications. Experience with Machine and Deep Learning libraries such as Scikit-learn, XGBoost, MXNet, TensorFlow or PyTorch Exposition to GenAI and solid understanding of multimodal AI via HuggingFace, Llama, VertexAI, AWS Bedrock or GPT Knowledge of data pipeline and More ❯
a production environment, MLOps and model integration into larger scale applications. Experience with Machine and Deep Learning libraries such as Scikit-learn, XGBoost, MXNet, TensorFlow or PyTorch Exposition to GenAI and solid understanding of multimodal AI via HuggingFace, Llama, VertexAI, AWS Bedrock or GPT Knowledge of data pipeline and More ❯
Artificial Intelligence, or a related field, or equivalent professional experience Strong programming skills in Python, with experience in machine learning frameworks such as PyTorch, TensorFlow or JAX Hands-on experience fine-tuning LLMs and optimising hyperparameters for improved performance Skilled in building LLM-powered applications for real-time decision More ❯
implementation. Contribute to internal initiatives such as blogs & technical forums. Requirements Substantial experience with Python and relevant libraries (e.g. Pandas, Numpy, Scikit Learn, PyTorch, Tensorflow). Experience driving ML & data science solutions into production. You can put the numbers into a business perspective - you're a data storyteller. You More ❯
applications, particularly in fraud prevention and user personalization. Experience designing, developing, and implementing advanced machine learning models. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Data Engineering Skills: Proficiency in developing and maintaining real-time data pipelines for processing large-scale data. Experience with ETL More ❯
SQL, MongoDB Experience on general programming languages: Python, C++, Java, etc. Experience with deep learning, machine learning and NLP frameworks such as PyTorch (or TensorFlow), HuggingFace Transformer, Scikit-learn Experience with working in Linux A strong intuition for what makes products a joy to use Empathy for how different More ❯
and multi-agent systems (MAS). Advanced Python programming skills, with a strong emphasis on writing efficient, scalable, and maintainable code. Proven experience with TensorFlow/PyTorch/Jax, Scikit-learn, and MLOps workflows for training, deployment, and monitoring of ML models. Experience working with Polars and/or More ❯
computer systems and how they operate. Excellent Python programming skills, including experience with relevant analytical and machine learning libraries (e.g., pandas, polars, numpy, sklearn, TensorFlow/Keras, PyTorch, etc.), in addition to visualization and API libraries (matplotlib, plotly, streamlit, Flask, etc). Experience developing and implementing quantitative models from More ❯
with advanced AI leaders. Strong programming skills in languages such as Python, Java, or C++. Experience with AI and machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn. What You'll Bring Preferred qualifications: Experience with cloud platforms such as AWS, Azure, or Google Cloud. Familiarity with data More ❯
PostgreSQL preferred but general SQL knowledge is more important). Familiarity with latest Data Science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and frameworks (e.g. Tensorflow, MXNet, scikit-learn). Knowledge of software engineering practices (coding practices to DS, unit testing, version control, code review). Experience with Hadoop (especially More ❯
business problems. We prioritise clean, well-tested code with a culture of documentation and knowledge sharing. Our tech stack includes GCP, Python, GitHub, PyTorch, TensorFlow, Scikit-learn, and XGBoost. With mature infrastructure and dedicated teams for Data Engineering, Analytics, and Platform Engineering, our Data Scientists enjoy high autonomy. We More ❯
bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms - demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. More ❯
AI. Proven expertise in deploying and managing Generative AI models (e.g., GPT, Stable Diffusion, BERT). Proficient in Python and ML libraries such as TensorFlow, PyTorch, or Hugging Face. Skilled in cloud platforms (AWS, GCP, Azure) and managed AI/ML services. Hands-on experience with Docker, Kubernetes, and More ❯
with training and evaluating BERT-like models in real-world applications, especially in NLP or classification problems. Hands on experience with ML tools like TensorFlow, PyTorch etc. Experience with data science libraries such as NLTK, Scikit-learn, SciPy, (Sci)SpaCy etc. Excellent problem-solving and programming skills in Python More ❯
/or large language models Proven experience with cloud platforms such as AWS, Azure, or Google Cloud Familiarity with tools and frameworks such as TensorFlow, PyTorch, MLflow, SageMaker, or Databricks Deep understanding of data architecture, APIs, and model deployment best practices Knowledge of MLOps and full model lifecycle management More ❯
business problems. We prioritise clean, well-tested code with a culture of documentation and knowledge sharing. Our tech stack includes GCP, Python, GitHub, PyTorch, TensorFlow, Scikit-learn, and XGBoost. With mature infrastructure and dedicated teams for Data Engineering, Analytics, and Platform Engineering, our Data Scientists enjoy high autonomy. We More ❯
such as fraud prevention or credit scoring. Machine Learning Expertise: Strong understanding of machine learning algorithms and their practical applications. Experience with frameworks like TensorFlow, PyTorch, and scikit-learn. Data Engineering: Proficiency in developing and maintaining real-time data pipelines. Experience with ETL processes, Python, and SQL. Familiarity with More ❯
such as fraud prevention or credit scoring. Machine Learning Expertise: Strong understanding of machine learning algorithms and their practical applications. Experience with frameworks like TensorFlow, PyTorch, and scikit-learn. Data Engineering: Proficiency in developing and maintaining real-time data pipelines. Experience with ETL processes, Python, and SQL. Familiarity with More ❯
MLOps, DevOps, or ML Engineering roles Proven expertise deploying and scaling Generative AI models (GPT, Stable Diffusion, BERT) Proficiency with Python and ML frameworks (TensorFlow, PyTorch, Hugging Face) Strong cloud platform experience (AWS, GCP, Azure) and managed AI/ML services Practical experience with Docker, Kubernetes, and container orchestration More ❯
