51 to 75 of 248 scikit-learn Jobs

and experience implementing ML lifecycle management (source control, CI/CD for models, model registries, etc.) on Azure or similar platforms. Proficiency in Python and common ML frameworks (scikit-learn, TensorFlow/PyTorch) and experience with code review and DevOps processes. Leadership & Communication: Excellent leadership and interpersonal skills. Able to interface with client stakeholders to explain complex AI More ❯

and experience implementing ML lifecycle management (source control, CI/CD for models, model registries, etc.) on Azure or similar platforms. Proficiency in Python and common ML frameworks (scikit-learn, TensorFlow/PyTorch) and experience with code review and DevOps processes. Leadership & Communication: Excellent leadership and interpersonal skills. Able to interface with client stakeholders to explain complex AI More ❯

we do require the ability to become a fluent Python programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

we do require the ability to become a fluent Python programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

impact of retail changes. Python Proficiency: Expert-level skills in Python, with demonstrated ability to build scalable, production-ready pricing and forecasting models using libraries such as pandas, scikit-learn, statsmodels, and PyMC. SQL Expertise: Expert-level skills in SQL for querying and manipulating large-scale retail and transactional datasets, with experience building automated data pipelines. Big Data More ❯

data science or a quantitative academic field. Strong programming background; must be able to quickly become fluent in Python . Proficiency with key data science libraries (NumPy, Pandas, Scikit-Learn) and experience with deep learning frameworks (TensorFlow, PyTorch, or Caffe). Strong mathematical and statistical skills. Broad knowledge of core data science techniques, such as: Supervised/unsupervised More ❯

years of experience applying data science in commercial settings Proven ability to lead data science projects from concept to production Strong Python skills (including libraries like Pandas, NumPy, Scikit-learn); experience with other languages is a plus Deep understanding of statistical modelling, predictive analytics, and optimisation techniques Comfortable working with cross-functional teams, including engineers, product leads, and More ❯

years of experience applying data science in commercial settings Proven ability to lead data science projects from concept to production Strong Python skills (including libraries like Pandas, NumPy, Scikit-learn); experience with other languages is a plus Deep understanding of statistical modelling, predictive analytics, and optimisation techniques Comfortable working with cross-functional teams, including engineers, product leads, and More ❯

discussions and implementing solutions defined by project leads. Desirable Experience Strong Python expertise. Experience with GNU/Linux environments. Familiarity with key data science and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, Hugging Face). Experience in natural language processing, tabular data analysis, or computer vision. SQL proficiency. Exposure to containerisation (Docker, Kubernetes) and cloud-native architectures. More ❯

discussions and implementing solutions defined by project leads. Desirable Experience Strong Python expertise. Experience with GNU/Linux environments. Familiarity with key data science and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, Hugging Face). Experience in natural language processing, tabular data analysis, or computer vision. SQL proficiency. Exposure to containerisation (Docker, Kubernetes) and cloud-native architectures. More ❯

managing complex, multi-faceted projects with competing deadlines. Expertise in Agile project management methodologies is a plus. Hands-on experience with a variety of machine learning frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch etc.) and AutoML technologies (e.g., Datarobot, Dataiku). Experience in deploying models at scale using cloud infrastructure is a plus. Proven ability to lead, mentor, and More ❯

NumPy). Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions, including Snowflake. More ❯

or other relevant AI fields. Proven track record of designing, developing, and deploying AI systems in production environments. Proficient in Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices More ❯

or other relevant AI fields. Proven track record of designing, developing, and deploying AI systems in production environments. Proficient in Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices More ❯

or other relevant AI fields. Proven track record of designing, developing, and deploying AI systems in production environments. Proficient in Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices More ❯

or other relevant AI fields. Proven track record of designing, developing, and deploying AI systems in production environments. Proficient in Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices More ❯

or other relevant AI fields. Proven track record of designing, developing, and deploying AI systems in production environments. Proficient in Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices More ❯

on experience in AI or ML in a professional environment (3-5 years preferred) • Strong knowledge of machine learning model development, deployment, and modern ML libraries (TensorFlow, PyTorch, scikit-learn, etc.) • Solid programming background with experience using statistical and signal analysis libraries • Experience with neural network architectures including deep learning models • Understanding of transformer architectures and attention mechanisms More ❯

date with emerging trends and technologies in the field of data science. Requirements Proven experience as a data scientist using Python and a range of libraries (Numpy, Pandas, Scikit-Learn, Matplotlib, Plotly etc.). Strong expertise in statistical modelling, machine learning, and data mining techniques. Experience in computer vision is essential. Data engineering (pipelines, databases, infrastructure), ideally with More ❯

qualifications: Hold an active Security clearance (Ideally Top Secret with SCI Poly) Strong programming skills in Python, with experience in machine learning libraries such as TensorFlow, PyTorch, or scikit-learn Proficient in data manipulation, feature engineering, and model evaluation techniques Solid understanding of different machine learning algorithms and their applications Experience deploying machine learning models into production environments More ❯

ML engineering, with a proven track record of successfully deploying and managing AI solutions in production environments. Proficiency in AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn). Experience with cloud-based AI platforms (e.g., AWS SageMaker, Google Cloud AI Platform, Azure Machine Learning). Experience with data management and processing tools (e.g., Hadoop, Spark More ❯

RAG), language translation, NER, summarization and topic modeling Familiarity with LLM training and efficient finetuning (PEFT, LoRA, Quantization) Experience with NLP/CV libraries such as PyTorch, TensorFlow, Scikit-learn, spaCy, HuggingFace Transformers/Diffusers, OpenAI, LangChain, Semantic Kernel & OpenCV Experience using Cloud solutions (AWS, Azure, etc.) for building and deploying applications Experience in a fast-paced, agile More ❯

background will include: 3+ years industry experience in a Data Science role and a strong academic background Python Data Science Stack: Advanced proficiency in Python , including pandas , NumPy , scikit-learn , and Jupyter Notebooks . Statistical & ML Modelling: Strong foundation in statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression More ❯

setting. Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written More ❯

setting. Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written More ❯