machine learning, particularly in recommendation systems and deep learning architectures Strong understanding of two-tower neural networks, embedding techniques, and ranking models Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku Experience with ML Ops, including deployment, monitoring, and retraining pipelines Ability to work More ❯
machine learning, particularly in recommendation systems and deep learning architectures Strong understanding of two-tower neural networks, embedding techniques, and ranking models Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku Experience with ML Ops, including deployment, monitoring, and retraining pipelines Ability to work More ❯
Python, R, or other programming languages commonly used in machine learning. Experience with libraries such as TensorFlow, PyTorch, scikit-learn, etc.Tools: Experience with data processing and manipulation tools (SQL, Pandas, Numpy), cloud platforms (AWS, Azure, GCP), and version control systems (GitDesirable Skills: Knowledge of deep learning, NLP, computer vision, or reinforcement learning.Experience in deploying machine learning models in production environments.Familiarity More ❯
visualisation libraries (Matplotlib, Seaborn.) SQL for data extraction and manipulation. Experience working with large datasets. Technical Skills Proficiency in cloud computing and python programming. Familiarity with Python libraries like Pandas, NumPy, scikit-learn. Experience with cloud services for mode training and deployment. Machine Learning Fundamentals Statistical concepts for robust data analysis. Linear algebra principles for modelling and optimisation. Calculus for More ❯
for system architecture, mentoring junior team members, and conducting thorough code reviews Strong programming skills in Python and C++, with experience using libraries and frameworks such as PyTorch, NumPy, Pandas, TensorFlow, and OpenCV for computer vision and data processing Familiarity with front-end technologies including JavaScript and HTML for building user-facing interfaces or tools Practical, hands-on experience in More ❯
City of London, London, United Kingdom Hybrid/Remote Options
Datatech Analytics
statistics and machine learning techniques (supervised and unsupervised learning, 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, and retraining pipelines. ? Ability More ❯
learning models at scale. Deep familiarity with 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 to work across data infrastructure, from More ❯
of AI/ML 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 principles and tools (Docker, Kubernetes, Git). Deep More ❯
London, South East, England, United Kingdom Hybrid/Remote Options
Method Resourcing
learning models at scale. Deep familiarity with 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 to work across data infrastructure, from More ❯
experience in the following areas are mandatory: 3 years of experience in data engineering and/or data architecture 2 years of experience with Python for ETL and automation (pandas, requests, API integration). 2 years hands-on experience with SQL queries, stored procedures, performance tuning (preferable Oracle, SQL Server, MySQL) 1 year experience with ETL orchestration tools (Prefect, Airflow More ❯
data cycle. - Proven Experience working with AWS data technologies (S3, Redshift, Glue, Lambda, Lake formation, Cloud Formation), GitHub, CI/CD - Coding experience in Apache Spark, Iceberg or Python (Pandas) - Experience in change and release management. - Experience in Database Warehouse design and data modelling - Experience managing Data Migration projects. - Cloud data platform development and deployment. - Experience of performance tuning in More ❯
/equivalent in Business Analytics & Information Systems, CIS, or related field; and 1 year of experience including SQL, Python, SAS, R, AWS Redshift, AWS S3, Jupyter Notebook, MicroStrategy, NumPy, Pandas, Airflow, S3, Tableau, and ServiceNow. $127,234. Medical, Dental, Vision. Position requires travel/relocation to various unanticipated US locations. Send resume to: Digtinctive Inc., 525 Washington Blvd., Suite More ❯
scale up? Having added nearly 100 people to the business this year, they're looking for Quantitative Developers with proficiency in Python, and experience with the Python data libraries (Pandas, NumPy) (preferably) from the Trading world. They also are on the look out for candidates who: Have deep familiarity with Python data ecosystem Understanding of Jupyter notebooks Exposure to machine More ❯
Central London, London, England, United Kingdom Hybrid/Remote Options
Opus Recruitment Solutions Ltd
scale up Having added nearly 100 people to the business this year, they're looking for Quantitative Developers with proficiency in Python, and experience with the Python data libraries (Pandas, NumPy) (preferably) from the Trading world. They also are on the look out for candidates who: Have deep familiarity with Python data ecosystem Understanding of Jupyter notebooks Exposure to machine More ❯
problems into technical solutions. Optimize models for scalability, performance, and accuracy. Mentor junior engineers and review code for quality and best practices. Required Skills & Experience Strong proficiency in Python (Pandas, NumPy, Scikit-learn, FastAPI/Flask). Experience with machine learning workflows: data preprocessing, model training, evaluation, and deployment preferably using Microsoft stack - Azure ML, Azure Data Factory, Synapse Analytics More ❯
integration and delivery of solutions. Optimize performance and reliability across data platforms and cloud environments. Document processes and share knowledge effectively. Essential Skills Python (Highly Proficient): Libraries such as Pandas, NumPy; API development (Flask/Dash); package management (pip, Poetry). DevOps Expertise: CI/CD tools (Jenkins, GitHub Actions), scripting (Bash, Python), Linux environments. Cloud & Containerization: Docker, Kubernetes, and More ❯
deployable AI solutions Key Skills & Experience Required Senior-level experience in data science or a quantitative field Proficient programming skills (Python preferred); familiarity with core data science libraries (NumPy, Pandas, Scikit-Learn) Experience with deep learning tools (e.g. PyTorch, TensorFlow, or similar) Strong mathematical/statistical background Familiarity with a wide range of data science methods (e.g. ML, NLP, Bayesian More ❯
Cheltenham, Gloucestershire, South West, United Kingdom Hybrid/Remote Options
Anson Mccade
deployable AI solutions Key Skills & Experience Required Senior-level experience in data science or a quantitative field Proficient programming skills (Python preferred); familiarity with core data science libraries (NumPy, Pandas, Scikit-Learn) Experience with deep learning tools (e.g. PyTorch, TensorFlow, or similar) Strong mathematical/statistical background Familiarity with a wide range of data science methods (e.g. ML, NLP, Bayesian More ❯
monitoring. Requirements Must Have: 5+ years of professional Python development experience. Strong ability to write clean, modern Python code. Experience building and documenting APIs. Deep knowledge of Python ecosystem (Pandas, Numpy, Bokeh, PyArrow, Matplotlib, IPyWidgets, Jupyter, etc.). Proven experience with large-scale data pipelines and ETL workflows. Comfortable working in Jupyter notebooks. Understanding of crypto or traditional financial markets. More ❯
trading constraints. Automate Data Pipelines, designing and managing workflows for collecting, cleaning, and storing large volumes of financial data (e.g., price, volume, fundamentals, alternative data), often using tools like Pandas, NumPy, and Dask. Collaborate Across Teams for Deployment, working with researchers, traders, and DevOps teams to integrate Python models into production environments (e.g., through APIs, microservices, or containerized systems like More ❯
Greater London, England, United Kingdom Hybrid/Remote Options
Hunter Bond
trading constraints. Automate Data Pipelines, designing and managing workflows for collecting, cleaning, and storing large volumes of financial data (e.g., price, volume, fundamentals, alternative data), often using tools like Pandas, NumPy, and Dask. Collaborate Across Teams for Deployment, working with researchers, traders, and DevOps teams to integrate Python models into production environments (e.g., through APIs, microservices, or containerized systems like More ❯
monitoring. Requirements Must Have: 5+ years of professional Python development experience. Strong ability to write clean, modern Python code. Experience building and documenting APIs. Deep knowledge of Python ecosystem (Pandas, Numpy, Bokeh, PyArrow, Matplotlib, IPyWidgets, Jupyter, etc.). Proven experience with large-scale data pipelines and ETL workflows. Comfortable working in Jupyter notebooks. Understanding of crypto or traditional financial markets. More ❯
