Multi-Asset and Insurance Work closely and collaboratively with our business partners to understand requirements and develop business focused solutions Advanced proficiency in Python with a strong focus on Pandas and PySpark Work in cross-functional agile teams to continuously experiment, iterate, and deliver on new objectives. Strong track record in working with open-source projects and picking the right More ❯
maintaining large-scale data pipelines using tools such as Databricks, Azure Data Factory, Snowflake, or similar Strong programming skills in Python and SQL, with proficiency in data engineering libraries (pandas, PySpark, dbt). Deep understanding of data modelling, ETL/ELT processes, and Lakehouse concepts. Experience with data quality frameworks, data governance, and compliance requirements. Familiarity with version control (Git More ❯
Contribute to the continuous improvement of development processes, including CI/CD and automated deployment pipelines. Required Skills & Experience Strong proficiency in Python, with experience in libraries such as Pandas, NumPy, and application frameworks (e.g., Flask, FastAPI, or similar). Solid understanding of software engineering principles, including object-oriented design and modular architecture. Experience building applications for front office environments More ❯
Essential Skills and Experience: Proven ability to solve complex, real-world problems through data science and analytics. Experience coaching and reviewing work of junior team members. Strong Python skills (pandas, numpy, scikit-learn) and a solid grounding in probability and statistics. Deep knowledge of machine learning methods and their practical application. Experience managing multiple end-to-end data science projects More ❯
ML models into production. Prior experience leading projects or teams is a plus for a lead role Programming & ML Skills: Advanced programming skills in Python (including libraries such as pandas, scikit-learn, TensorFlow/PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps More ❯
ML models into production. Prior experience leading projects or teams is a plus for a lead role Programming & ML Skills: Advanced programming skills in Python (including libraries such as pandas, scikit-learn, TensorFlow/PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps 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 ❯
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 ❯
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 ❯
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 ❯
Working knowledge of advanced predictive modeling, optimization, scenario analysis, and statistical methodologies. Strong grasp of supervised/unsupervised methods, evaluation metrics, feature engineering, and model tuning. Proficiency in Python (pandas, NumPy, Scikit-learn); experience with PyTorch or TensorFlow for deep learning. Experience with API development and connecting AI systems to external platforms. Working knowledge in deep learning techniques, including CNNs More ❯
and building reusable analytical datasets. Modern BI: hands-on experience with a modern BI platform with integrated semantic layer (e.g Omni, Looker, Thoughtspot) Python for analysis: practical experience with pandas/numpy and notebook-based workflows for LTV, attribution, simulations and experimentation analysis. Domain expertise: deep, practical knowledge in one or more of the following functions & platforms: CRM (e.g. SalesForce More ❯
and building reusable analytical datasets. Modern BI: hands-on experience with a modern BI platform with integrated semantic layer (e.g Omni, Looker, Thoughtspot) Python for analysis: practical experience with pandas/numpy and notebook-based workflows for LTV, attribution, simulations and experimentation analysis. Domain expertise: deep, practical knowledge in one or more of the following functions & platforms: CRM (e.g. SalesForce More ❯
recommendation systems, personalization, natural language processing (NLP), or semantic search. Expert-level programming skills in Python, with deep, hands-on experience using data science and ML libraries such as Pandas, Scikit-learn, TensorFlow, or PyTorch. Experience with data storage technologies (e.g., SQL, NoSQL, Key-value) and their scaling characteristics. Experience with large-scale data processing technologies (e.g., Spark, Beam, Flink More ❯
Develop dashboards and visualizations for business teams Communicate findings to non-technical stakeholders What You Bring 5+ years in applied data science or analytics Strong Python or R skills (pandas, NumPy, Jupyter) and solid SQL experience Familiarity with BI tools like Tableau or Power BI Strong foundation in statistics, experimentation, and A/B testing Excellent communication and storytelling abilities More ❯
Develop dashboards and visualizations for business teams Communicate findings to non-technical stakeholders What You Bring 5+ years in applied data science or analytics Strong Python or R skills (pandas, NumPy, Jupyter) and solid SQL experience Familiarity with BI tools like Tableau or Power BI Strong foundation in statistics, experimentation, and A/B testing Excellent communication and storytelling abilities More ❯
design, rubric structure, and evaluation criteria. Communicate insights to data labeling experts and technical teams. Requirements Strong foundation in statistical analysis, hypothesis testing, and pattern recognition. Proficiency in Python (pandas, scipy, matplotlib/seaborn) or R for data analysis. Experience with exploratory data analysis and deriving actionable insights from complex datasets. Familiarity with LLM evaluation methods and quality metrics. Comfortable More ❯
models (GAN, VAE) to 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 to operationalise models through containerised MLOps environments (Kubernetes More ❯
re Looking For You've led the delivery of applied machine learning projects, ideally across commercial or regulated sectors Strong Python skills and comfort using core libraries (e.g. NumPy, Pandas), plus familiarity with deep learning tooling like PyTorch Expertise in a wide range of ML methods, including supervised and unsupervised learning, time series, or NLP and LLM/GenAI based More ❯
Good Fit If You Have Strong software engineering skills, with proficiency in Python and experience building data pipelines. Familiarity with data processing frameworks such as Apache Spark, Apache Beam, Pandas, or similar tools. Experience working with large-scale web datasets like CommonCrawl. A passion for bridging research and engineering to solve complex data-related challenges in AI model training. Bonus More ❯
