1 to 25 of 101 Permanent scikit-learn Jobs

or related field. 7+ years of professional software development experience, with at least 3 years in AI/ML. Strong proficiency in Python , including libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch . Solid understanding of ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . More ❯

Looking For: 🎓 A degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field 🐍 Proficiency in Python and familiarity with libraries such as TensorFlow, PyTorch, or scikit-learn 🧩 Understanding of machine learning algorithms and statistical modelling 📈 Strong problem-solving and analytical mindset 🗣 Excellent communication skills and a genuine passion for learning Nice to Have: 💾 Experience with More ❯

Looking For: 🎓 A degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field 🐍 Proficiency in Python and familiarity with libraries such as TensorFlow, PyTorch, or scikit-learn 🧩 Understanding of machine learning algorithms and statistical modelling 📈 Strong problem-solving and analytical mindset 🗣 Excellent communication skills and a genuine passion for learning Nice to Have: 💾 Experience with More ❯

Essential - Demonstrable experience in Machine Learning, NLP, and GenAI including LLM/SLM fine-tuning and prompt development. Proficiency in Python and data libraries such as Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow. Practical knowledge in SQL/NoSQL databases and API development/integration. Proven ability to analyze model performance and optimize for accuracy, efficiency, and scalability. More ❯

Master’s degree from a Russell Group university in Data Science, Computer Science, Mathematics, Physics, Engineering, or a related discipline Strong programming skills in Python (e.g. NumPy, pandas, scikit-learn, matplotlib); R is also welcome A solid understanding of core machine learning concepts, data wrangling, and model evaluation Proficiency with SQL and experience handling large datasets A passion More ❯

Master’s degree from a Russell Group university in Data Science, Computer Science, Mathematics, Physics, Engineering, or a related discipline Strong programming skills in Python (e.g. NumPy, pandas, scikit-learn, matplotlib); R is also welcome A solid understanding of core machine learning concepts, data wrangling, and model evaluation Proficiency with SQL and experience handling large datasets A passion More ❯

AI, with a strong portfolio of high-impact projects in production Expert-level programming skills in Python and SQL, and fluency with leading ML/AI frameworks (e.g., scikit-learn, TensorFlow, PyTorch) Direct experience with GenAI/LLM technologies, including tools like Hugging Face, LangChain, OpenAI APIs, vector databases, and fine-tuning methods Deep knowledge of machine learning More ❯

LangChain, TensorFlow, and PyTorch Practical experience with Generative AI and exposure to leading LLM platforms (Anthropic, Meta, Amazon , OpenAI) Proficiency with essential data science libraries including Pandas, NumPy, scikit-learn, Plotly/Matplotlib, and Jupyter Notebooks Knowledge of ML-adjacent technologies, including AWS SageMaker, Kedro and MLflow. Strong skills in data preprocessing, wrangling, and augmentation techniques Experience deploying More ❯

AI, with a strong portfolio of high-impact projects in production Expert-level programming skills in Python and SQL, and fluency with leading ML/AI frameworks (e.g., scikit-learn, TensorFlow, PyTorch) Direct experience with GenAI/LLM technologies, including tools like Hugging Face, LangChain, OpenAI APIs, vector databases, and fine-tuning methods Deep knowledge of machine learning More ❯

ML, data science, or applied statistics Solid programming skills in Python (or similar languages) Familiarity with data science or AI libraries such as Pandas, NumPy, TensorFlow, PyTorch, or Scikit-learn Strong problem-solving ability and analytical thinking A willingness to learn and work collaboratively in a team environment Nice to Have (But Not Required): Experience from a More ❯

ML, data science, or applied statistics Solid programming skills in Python (or similar languages) Familiarity with data science or AI libraries such as Pandas, NumPy, TensorFlow, PyTorch, or Scikit-learn Strong problem-solving ability and analytical thinking A willingness to learn and work collaboratively in a team environment Nice to Have (But Not Required): Experience from a More ❯

ML, data science, or applied statistics Solid programming skills in Python (or similar languages) Familiarity with data science or AI libraries such as Pandas, NumPy, TensorFlow, PyTorch, or Scikit-learn Strong problem-solving ability and analytical thinking A willingness to learn and work collaboratively in a team environment Nice to Have (But Not Required): Experience from a More ❯

ML, data science, or applied statistics Solid programming skills in Python (or similar languages) Familiarity with data science or AI libraries such as Pandas, NumPy, TensorFlow, PyTorch, or Scikit-learn Strong problem-solving ability and analytical thinking A willingness to learn and work collaboratively in a team environment Nice to Have (But Not Required): Experience from a More ❯

as a Data Scientist, ideally within customer analytics, marketing, or CRM environments. Strong coding skills in Python, with deep familiarity across ML and data libraries (pandas, numpy, scipy, scikit-learn, tensorflow, pytorch). A broad understanding of machine learning and statistical methods, including NLP, deep learning, Bayesian inference, time-series forecasting, and recommendation systems. Hands-on experience with More ❯

as a Data Scientist, ideally within customer analytics, marketing, or CRM environments. Strong coding skills in Python, with deep familiarity across ML and data libraries (pandas, numpy, scipy, scikit-learn, tensorflow, pytorch). A broad understanding of machine learning and statistical methods, including NLP, deep learning, Bayesian inference, time-series forecasting, and recommendation systems. Hands-on experience with More ❯

technical field Strong proficiency in Python is a must 2 - 4 years of experience in machine learning or backend software development Experience using frameworks like TensorFlow, PyTorch, or Scikit-learn Solid understanding of ML workflows: data cleaning, model development, tuning, evaluation Familiar with model deployment, API development, or real-world ML integration Experience with tools like Google Colab 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 ❯

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 ❯

or technical field (Statistics, Mathematics, Physics, Computer Science, Machine Learning) Strong programming skills 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) Strong experience with data visualisation tools and techniques; able to turn complex More ❯

TensorFlow and PyTorch. Programming: Solid experience in Python and SQL. Experience with R is a nice-to-have. ML and AI: Practical experience using ML modeling libraries like Scikit-Learn, Keras, Tensorflow, PyTorch and similar Generative AI: Some hands-on experience with LLMs for prompt engineering or agents is preferred Cloud Expertise: Building, deploying and monitoring models on More ❯

experience with model deployment and monitoring tools, version control, and orchestration frameworks (e.g., Docker, Kubernetes, MLflow, CI/CD pipelines). Experience with Python and common ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost) and data-processing tools (SQL, Spark). Typical Education & Experience Experienced (Level 3) Education/experience typically acquired through advanced education (e.g. Associate) and typically More ❯

experience with model deployment and monitoring tools, version control, and orchestration frameworks (e.g., Docker, Kubernetes, MLflow, CI/CD pipelines). Experience with Python and common ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost) and data-processing tools (SQL, Spark). Typical Education & Experience Experienced (Level 3) Education/experience typically acquired through advanced education (e.g. Associate) and typically More ❯

package Key Responsibilities: Research and deploy LLM-based solutions (e.g., LangChain, Mastra.ai, Pydantic) for document processing, summarization, and clinical Q&A systems. Develop and optimize predictive models using scikit-learn, PyTorch, TensorFlow, and XGBoost. Design robust data pipelines using tools like Spark and Kafka for real-time and batch processing. Manage ML lifecycle with tools such as Databricks More ❯

them to strong results. Experienced in engaging with non-technical partners to scope, design and build an appropriate ML solution. Proficient with Python data science stack, e.g., pandas, scikit-learn, Jupyter etc., and version control, e.g., Git. Knowledge of OO programming, software design, i.e., SOLID principles, and testing practices. Knowledge and working experience of AGILE methodologies. Proficient with More ❯

them to strong results. Experienced in engaging with non-technical stakeholders to scope, design and build an appropriate ML solution. Proficient with Python data science stack, e.g., pandas, scikit-learn, Jupyter etc., and version control, e.g., Git. Exposure to LLMOps, model monitoring principles, CI/CD and associated tech, e.g., Docker, MLflow, k8s, FastAPI etc. Knowledge of Langchain More ❯