1 to 25 of 136 scikit-learn Jobs

technical processes. Required Skills & Qualifications:- Classic Machine learning (Regression, predictive Analysis, Classification, Clustering) Machine learning Model Optimisation Strong proficiency in Python (NumPy, Pandas, Scikit-learn). Hands-on experience with Deep Learning frameworks: TensorFlow, PyTorch, or Keras. Experience in Natural Language Processing (NLP) and/or Computer Vision. ...

production-grade analytical solutions. Hands-on experience with cloud platforms (Azure, AWS and/or Snowflake). Expertise in modern machine learning frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow). Demonstrable experience delivering end-to-end ML and GenAI solutions (including RAG, LLMs, embeddings and vector databases). Strong ...

experience with LLMs, generative AI, NLP, or advanced modelling techniques. Proficiency in Python, SQL and modern data science libraries (e.g., PyTorch, TensorFlow, scikit-learn, HuggingFace). Experience mentoring or managing data scientists. Confident working in Agile environments with Git/version control. Strong communication skills, especially with ...

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 ...

contribute to our internal library of proprietary tools and frameworks. Technical Profile Expert Programming: Mastery of Python and its ecosystem (Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow). Experience writing production-ready, modular, and well-tested code. Advanced Modeling: Deep theoretical and practical knowledge of Machine Learning. ...

GenAI: Experience with LLM APIs and Context Engineering (constructing prompts, managing context windows, evaluating behaviour). Engineering & Stack: Python: Expert level (Pandas, NumPy, Scikit-Learn). Data Engineering: Strong SQL skills and experience building data pipelines. Experience: Education: Degree in a quantitative field (Statistics, Mathematics, Computer Science, etc. ...

exposure to ML lifecycle. Hands-on experience building or maintaining ML workflows or pipelines. Strong Python skills and experience with tools like MLflow, Scikit-learn, or PyTorch. Familiarity with cloud platforms (especially Azure, AWS). Good understanding of containerisation (Docker) and orchestration (e.g. Kubernetes). Exposure ...

with modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research ...

both technical and business stakeholders Nice to Have: Advanced degree in data science or a related field Experience with ML frameworks (TensorFlow, Keras, scikit-learn, etc.) Docker and Kubernetes experience Exposure to AWS SageMaker, Azure ML, MLflow, or enterprise ML platforms Open-source contributions or relevant side projects ...

both technical and business stakeholders Nice to Have: Advanced degree in data science or a related field Experience with ML frameworks (TensorFlow, Keras, scikit-learn, etc.) Docker and Kubernetes experience Exposure to AWS SageMaker, Azure ML, MLflow, or enterprise ML platforms Open-source contributions or relevant side projects ...

both technical and business stakeholders Nice to Have: Advanced degree in data science or a related field Experience with ML frameworks (TensorFlow, Keras, scikit-learn, etc.) Docker and Kubernetes experience Exposure to AWS SageMaker, Azure ML, MLflow, or enterprise ML platforms Open-source contributions or relevant side projects ...

both technical and business stakeholders Nice to Have: Advanced degree in data science or a related field Experience with ML frameworks (TensorFlow, Keras, scikit-learn, etc.) Docker and Kubernetes experience Exposure to AWS SageMaker, Azure ML, MLflow, or enterprise ML platforms Open-source contributions or relevant side projects ...

both technical and business stakeholders Nice to Have: Advanced degree in data science or a related field Experience with ML frameworks (TensorFlow, Keras, scikit-learn, etc.) Docker and Kubernetes experience Exposure to AWS SageMaker, Azure ML, MLflow, or enterprise ML platforms Open-source contributions or relevant side projects ...

both technical and business stakeholders Nice to Have: Advanced degree in data science or a related field Experience with ML frameworks (TensorFlow, Keras, scikit-learn, etc.) Docker and Kubernetes experience Exposure to AWS SageMaker, Azure ML, MLflow, or enterprise ML platforms Open-source contributions or relevant side projects ...

both technical and business stakeholders Nice to Have: Advanced degree in data science or a related field Experience with ML frameworks (TensorFlow, Keras, scikit-learn, etc.) Docker and Kubernetes experience Exposure to AWS SageMaker, Azure ML, MLflow, or enterprise ML platforms Open-source contributions or relevant side projects ...

Strong knowledge of machine learning and/or optimisation techniques (Regression, Tree methods, Clustering, Linear/Mixed-Integer Programming, Heuristics) Strong Python experience (scikit-learn, pandas, numpy, optimisation libraries) Experience building production-ready ML or optimisation solutions Solid understanding of CI/CD , Git version control, and cloud ...

closely related field Strong foundations in machine learning, algorithms, and statistics Experience with Python and common ML libraries (e.g. TensorFlow, PyTorch, scikit-learn) Understanding of software engineering fundamentals and version control (e.g. Git) Ability to communicate technical ideas clearly to both technical and non-technical stakeholders Right ...

enterprise environments Deep understanding of algorithm selection, model complexity trade-offs, and overfitting/underfitting dynamics Strong proficiency in Python and ML ecosystems (scikit-learn, pandas, NumPy) Experience with deep learning frameworks (PyTorch or TensorFlow) Practical knowledge of deep learning architectures (CNNs, RNNs, Transformers) and when to apply ...

experience delivering end-to-end ML or AI solutions into production environments* Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn* Experience working with LLMs, including prompt design, RAG, or fine-tuning* Experience building APIs and integrating AI systems into real products* Solid understanding ...

systems, personalisation, or deep learning is desirable but not essential. Solid Python skills and experience with ML libraries such as pandas, numpy, scipy, scikit-learn, TensorFlow or PyTorch. Experience working across cloud environments (GCP, AWS, or Azure) and analytics platforms such as Dataiku. Good understanding of MLOps practices ...

Engineer will have the following skills and experience: Strong Python and SQL experience, ideally with Databricks Experience with ML and LLM tools (e.g., scikit learn, XGBoost, PyTorch, TensorFlow, RAG, APIs) Comfortable with data engineering basics and MLOps practices Experience working with cloud platforms such as Azure ...

conversational AI, recommendation systems, optimisation, or marketing analytics. Deep understanding of ML fundamentals across the full lifecycle. Hands‐on experience with ML frameworks (scikit‐learn, XGBoost, PyTorch, TensorFlow, LightGBM). Strong exposure to LLMs, prompt engineering, RAG, and evaluation frameworks. Familiarity with MLOps tooling: CI/CD, experiment ...

related field; 3+ years of experience in machine learning. Strong programming skills in Python, with experience using data and ML libraries (pandas, scikit-learn, etc.). Proficiency in SQL and data querying from cloud data warehouses. Ensure data quality and integrity in all processes. Experience with building backend ...

enterprise environments Deep understanding of algorithm selection, model complexity trade-offs, and overfitting/underfitting dynamics Strong proficiency in Python and ML ecosystems (scikit-learn, pandas, NumPy) Experience with deep learning frameworks (PyTorch or TensorFlow) Practical knowledge of deep learning architectures (CNNs, RNNs, Transformers) and when to apply ...

science practices tooling governance Essential Skills for the Data Scientist: Strong experience in data science using Python with libraries such as Pandas NumPy scikit-learn or similar Solid grounding in statistics probability machine learning techniques Proven experience working within cloud environments such as AWS Azure or GCP Experience ...