1 to 25 of 342 scikit-learn Jobs

focused on AI and ML system development. Strong expertise in machine learning algorithms, deep learning frameworks (TensorFlow, PyTorch, Keras), and classical ML tools (Scikit-learn Hands-on experience building production-grade AI and data pipelines using cloud platforms (AWS, Azure, or Google Cloud Proficiency in Python ...

equivalent. Exposure to observability/monitoring systems (Prometheus, Grafana, ELK, Datadog) is a plus. Proficiency in Python and familiarity with ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch). Strong experience with data manipulation and pipelines using Pandas, NumPy, and SQL. Knowledge of containerized deployments (Docker, Kubernetes) and cloud ...

looking for someone with a strong technical foundation, an innovative mindset, and a collaborative spirit. Technical skills (essential): Expertise in Python (NumPy, pandas, scikit-learn) and SQL Experience with EDA, data wrangling, and visualization tools (e.g., Matplotlib, Plotly, BI tools) Ability to build, evaluate, and deploy ML models ...

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 ...

practices. Qualifications Advanced proficiency in Python (specifically for machine learning) and extensive experience with core AI/ML open-source libraries, including scikit-learn, PyTorch, pandas, polars, NumPy, and seaborn. Proven experience designing and deploying end-to-end AI/ML systems, with a strong emphasis on MLOps ...

automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation ...

automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation ...

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 ...

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 ...

Solutions Engineer AI Solutions, B2B, B2C, Azure, AI Foundry, Open-AI, Microsoft Copilot Studio, Machine Learning, Python, TensorFlow, PyTorch, scikit-learn, Large Language Models, LLM, Data preprocessing, REST API, Microservices architecture, MLOps, CI/CD for ML, Power-BI, Docker, Kubernetes, AI Ethics, Cloud Platforms, AWS, Google Cloud ...

natural language processing, Bayesian statistics, time-series forecasting, collaborative filtering etc) ? Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) ? Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku, Databricks. ? Experience with ML Ops, including model deployment, monitoring ...

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 ...

core ML concepts (classification, time-series, statistical modeling) and their real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability ...

tech. What We’re Looking For 5+ years in data engineering, with team or project leadership experience. Advanced Python (Pandas, PyTorch, TensorFlow, Scikit-learn). Strong AWS/GCP , MySQL , and CI/CD experience; Docker/Kubernetes a plus. Excellent communication, organisation, and problem-solving skills. Passion ...

proficiency in Python 2-4 years of experience in machine learning or backend software development Hands on experience with ML frameworks (TensorFlow, PyTorch, Scikit learn) Solid understanding of ML workflows: data cleaning, model development, tuning, evaluation Familiarity with model deployment, API development, or real world ML product integration ...

DevOps practices (Infrastructure-as-Code). Proficiency with Git, Unix/Linux, Docker. Familiarity with ML frameworks such as TensorFlow, PyTorch, Keras, or scikit-learn (commercial experience not essential). Excellent communication skills for both internal and client-facing interactions. Awareness of MLOps is a plus. UK citizenship ...

DevOps practices (Infrastructure-as-Code). Proficiency with Git, Unix/Linux, Docker. Familiarity with ML frameworks such as TensorFlow, PyTorch, Keras, or scikit-learn (commercial experience not essential). Excellent communication skills for both internal and client-facing interactions. Awareness of MLOps is a plus. UK citizenship ...

competition performance 3–5+ years of experience in data science or applied analytics Strong proficiency in Python and data tools (Pandas, NumPy, Polars, scikit-learn, etc.) Experience building ML models end-to-end: feature engineering, training, evaluation, and deployment Solid understanding of statistical methods, experiment design, and causal ...

competition performance 3–5+ years of experience in data science or applied analytics Strong proficiency in Python and data tools (Pandas, NumPy, Polars, scikit-learn, etc.) Experience building ML models end-to-end: feature engineering, training, evaluation, and deployment Solid understanding of statistical methods, experiment design, and causal ...

competition performance 3–5+ years of experience in data science or applied analytics Strong proficiency in Python and data tools (Pandas, NumPy, Polars, scikit-learn, etc.) Experience building ML models end-to-end: feature engineering, training, evaluation, and deployment Solid understanding of statistical methods, experiment design, and causal ...

competition performance 3–5+ years of experience in data science or applied analytics Strong proficiency in Python and data tools (Pandas, NumPy, Polars, scikit-learn, etc.) Experience building ML models end-to-end: feature engineering, training, evaluation, and deployment Solid understanding of statistical methods, experiment design, and causal ...

competition performance 3–5+ years of experience in data science or applied analytics Strong proficiency in Python and data tools (Pandas, NumPy, Polars, scikit-learn, etc.) Experience building ML models end-to-end: feature engineering, training, evaluation, and deployment Solid understanding of statistical methods, experiment design, and causal ...

competition performance 3–5+ years of experience in data science or applied analytics Strong proficiency in Python and data tools (Pandas, NumPy, Polars, scikit-learn, etc.) Experience building ML models end-to-end: feature engineering, training, evaluation, and deployment Solid understanding of statistical methods, experiment design, and causal ...

competition performance 3–5+ years of experience in data science or applied analytics Strong proficiency in Python and data tools (Pandas, NumPy, Polars, scikit-learn, etc.) Experience building ML models end-to-end: feature engineering, training, evaluation, and deployment Solid understanding of statistical methods, experiment design, and causal ...

competition performance 3–5+ years of experience in data science or applied analytics Strong proficiency in Python and data tools (Pandas, NumPy, Polars, scikit-learn, etc.) Experience building ML models end-to-end: feature engineering, training, evaluation, and deployment Solid understanding of statistical methods, experiment design, and causal ...