1 to 25 of 38 Remote/Hybrid Permanent scikit-learn Jobs

setting. Education: Relevant Software Engineering/Computer Science degree. Technical Skills Programming Languages: Python, Java, C++, C#, TypeScript, JavaScript Machine Learning: TensorFlow, PyTorch, Scikit-learn GenAI/LLMs: OpenAI API, Hugging Face Transformers, LangChain, Langraph, RAG pipelines Data & Analytics: Pandas, NumPy, Spark, Databricks, Hadoop, Vector Databases, Snowflake Model ...

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. ...

degree (Russell Group preferred) in Data Science, Computer Science, Mathematics, Physics, Engineering, or similar Excellent Python skills and ML foundations (e.g. pandas, NumPy, scikit-learn; strong coding practices matter) Strong grasp of model evaluation, statistics, and experimentation Confident working with data at scale using SQL (and a practical ...

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. ...

patterns, demand profiles, and commercial optimisation. Required Skills and Experience Technical Skills Proficiency in Python and associated data science libraries (NumPy, Pandas, SciPy, scikit‐learn, PyTorch/TensorFlow). Strong experience with time‐series modelling (ARIMA, Prophet, LSTMs or similar). Understanding of optimisation methods (linear, mixed‐integer ...

technical stakeholders What we’re looking for Essential experience Strong grounding in statistical modelling, experimentation and inference Advanced Python skills (NumPy, pandas, scikit-learn, PyTorch or TensorFlow) Experience building, deploying and optimising ML models in production Strong AWS experience (e.g. SageMaker, Lambda or similar services) Expert SQL skills ...

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 ...

Required Skills: Experience in data science, machine learning, or applied analytics Strong proficiency in Python and common ML libraries (pandas, NumPy, scikit‐learn, TensorFlow, PyTorch) Experience designing, training, and evaluating machine‐learning models Solid understanding of statistical methods, predictive modelling, and experimental design Experience working with large ...

Python Understanding of machine learning fundamentals (supervised/unsupervised learning, model evaluation, overfitting, etc.) Experience with common ML libraries such as PyTorch, TensorFlow, scikit-learn , or similar Solid foundation in data structures, algorithms, and software engineering principles Strong problem-solving skills and attention to detail Excellent communication skills ...

data analysis or machine learning (internships, placements, academic or commercial projects) 🐍 Strong Python fundamentals and exposure to libraries such as pandas, NumPy, and scikit-learn 🧮 A solid understanding of statistics, data wrangling, and model evaluation 🗄 Confidence working with SQL and structured datasets ☁️ Familiarity with cloud platforms ...

into production environments. Key skills and experience include: Strong programming skills in Python Experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn Advanced knowledge of statistics, probability, and optimisation techniques Proven experience building, testing, tuning, and optimising machine learning models Comfortable working with large, complex ...

such as NLP, computer vision, audio, graphs, time-series or tabular data Strong Python skills and experience with common libraries (e.g. pandas, NumPy, scikit-learn, PyTorch, Transformers, statsmodels, PyMC) Understanding of software engineering and ML best practices, including version control, packaging and model deployment Experience training and deploying ...

time-series forecasting and spatio-temporal modeling, ideally applied to weather or energy systems. Strong proficiency in Python (Pandas, NumPy, PyTorch, TensorFlow, scikit-learn, Lightning, or equivalent). Solid understanding of data engineering and ML infrastructure Experience deploying ML models to cloud-native environments (Kubernetes, Docker, AWS/ ...

datasets, data preprocessing, feature engineering, and efficient data flow to ensure high-quality input for machine learning models. Optimizing models. Use tools like scikit-learn and Keras to fine-tune models for enhanced model performances Perform exploratory data analysis to identify patterns and insights. Develop and test statistical ...

years' experience as a data scientist in industry or related environments. Proficiency in Python and common ML/data libraries (e.g., pandas, numpy, scikit-learn, PyTorch, transformers). Understanding of software and ML engineering best practices (git, package development, model deployment). Experience with cloud platforms, particularly ...

junior colleagues and share learnings across the team. Requirements: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow. Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments. Strong experiment design skills: hypothesis formulation ...

Skilled at working with non-technical stakeholders to scope, design and deliver effective ML solutions. Proficient with the Python data science stack (pandas, scikit-learn, Jupyter, etc.) and version control tools like Git. Exposure to LLMOps, model monitoring, CI/CD and related technologies (Docker, MLflow, Kubernetes, FastAPI ...

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 ...

solutions Implement experiment tracking and monitoring Collaborate with data scientists and engineers What We're Looking For: Proficiency in Python and ML frameworks (scikit-learn, PyTorch, TensorFlow) Hands-on experience with AWS ML services (SageMaker, Lambda) Familiarity with containerisation (Docker) and orchestration (Kubernetes/ECS) Knowledge of LLMOps ...

junior data scientists and help shape AI best practice Requirements Strong experience in AI, machine learning and statistics Python expertise (e.g. pandas, NumPy, scikit-learn, PyTorch, transformers) Experience deploying models and working with cloud platforms (AWS preferred) Ability to communicate complex technical concepts clearly Desirable AWS services (SageMaker ...

looking for Strong experience with Python and SQL (model building, analysis, visualisation) Solid understanding of data science techniques and libraries (Pandas, scikit-learn, supervised & unsupervised models, tree-based models) Proven experience with customer analytics (e.g. churn, CLTV, segmentation) - required Hands-on experience deploying ML models into production Exposure ...

years' experience in ML/AI engineering or data science roles (senior level). Hands-on experience with Python ML frameworks (scikit-learn, PyTorch, TensorFlow, XGBoost). Experience with AWS ML services (SageMaker, Lambda, S3) and MLOps/LLMOps tooling (MLflow, Weights & Biases, DVC). Proven track record ...

with support from more experienced colleagues. Some experience presenting technical findings to non-technical stakeholders. Proficient with the Python data science stack (pandas, scikit-learn, Jupyter, etc.) and version control tools like Git. Any exposure to LLMOps, model monitoring, CI/CD and related technologies (Docker, MLflow, Kubernetes ...

engineers to productionise models Communicate insights to technical and non-technical stakeholders Mentor junior team members Skillset: Strong Python and ML libraries (scikit-learn, PyTorch, pandas, NumPy) Experience with AWS cloud services (SageMaker, Lambda, S3) Familiarity with MLOps tools (MLflow, Weights & Biases) Knowledge of NLP, computer vision ...

stakeholders Mentor junior engineers and contribute to team best practices This role is for you if you have: Strong Python ML experience (e.g. scikit-learn, XGBoost, PyTorch, or TensorFlow) Production experience with AWS ML services (e.g. SageMaker, Lambda, S3) Proven ability to move models from experimentation into live ...