18 of 18 Remote Pandas Jobs in Central London

Snowflake, AWS, and Python Work directly with investment professionals to scope and deliver solutions What we’re looking for: Strong Python engineering skills, including use of data libraries like pandas and NumPy Experience working in front office environments Familiarity with data pipelines and cloud infrastructure Pragmatic, detail-oriented, and able to work independently Experienced Python-focused Software/Data Engineer More ❯

utilising cloud platforms such as AWS, Azure and Snowflake for scalable deployment. Key Responsibilities: Develop and optimise quantitative models and analytical tools in Python Leverage statistical g packages (e.g., pandas, NumPy) for data analysis and modelling Work closely with portfolio managers, researchers, and technology teams to deploy robust, scalable solutions Design and implement data pipelines and workflows utilising AWS or More ❯

Python Liaise daily with investment professionals to understand requirements and deliver solutions What we’re looking for: Strong Python developer with deep experience using statistical and numerical libraries (NumPy, pandas, SciPy etc.) Proven experience supporting quant or systematic investment strategies Good understanding of infrastructure – ideally AWS and Snowflake – and willingness to own setup and tooling Comfortable working in fast-paced More ❯

Data Science related role Proven depth in probability & inference (e.g., Bayesian updating, time-series/state-space models, extreme-value theory). Hands-on Python for numerical analysis (numpy, pandas, xarray, SciPy/PyMC/PyTorch, or similar). Experience validating models with historical data and communicating results to non-specialists. Exposure to real-time data engineering (Kafka, Airflow, dbt More ❯

Proficiency with AWS services such as Lambda, DynamoDB, S3, SNS, SQS, and Serverless EMR Experience with SQL and NoSQL databases Hands-on Python experience, particularly with data libraries (e.g., pandas, scikit-learn) Familiarity with RESTful APIs and event-driven architectures Strong debugging, system design, and testing skills (e.g., JUnit) Passion for building scalable, reliable platforms supporting real-world use cases More ❯

complex, multi-step workflows with tools such as Airflow, Prefect, or Temporal. Comfortable working at the intersection of science and engineering, with familiarity in libraries like NumPy, SciPy, and pandas, and experience handling academic literature and data formats. Bonus Qualifications Background in academic research or research software engineering. Experience in scientific automation, document parsing, OCR, and data extraction from research More ❯

Collaborating with stakeholders across trading desks Tech Stack & Experience Required: Strong Python skills in a trading environment (ideally front office) Solid experience with React or Angular Experience with NumPy, Pandas , async Python features (bonus) Exposure to Docker , GraphQL (or similar technologies) Excellent communication skills – must be confident working closely with stakeholders Bonus (Nice-to-Have): Familiarity with systematic trading Offer More ❯

recently completed Master’s degree from a Russell Group university in Data Science, Computer Science, Mathematics, Physics, Engineering, or a related discipline Strong programming skills in Python (e.g. NumPy, pandas, scikit-learn, matplotlib); R is also welcome A solid understanding of core machine learning concepts, data wrangling, and model evaluation Proficiency with SQL and experience handling large datasets A passion More ❯

lending strategy (SME or consumer lending preferred). Strong quantitative background (degree in Maths, Engineering, Physics, or similar). Experience building and maintaining predictive credit models. Fluency in Python (Pandas, NumPy, SciPy, Matplotlib) and SQL. Experience with advanced modelling techniques (e.g., Monte Carlo, Bayesian modelling) is a plus. Strong communicator. Commercial mindset and strong instincts around risk and return. Experience More ❯

s or PhD degree in a quantitative field. • Proven experience of large-scale data analysis and hypothesis testing. • Strong proficiency in statistical analysis and predictive modeling. • Proficient in Python (pandas, scipy, numpy, scikit-learn) or R (tidyverse/data.table), along with SQL. • Excellent problem-solving skills and attention to detail. • Strong communication skills with the ability to present complex data More ❯

AWS/GCP Key skills and requirements: Demonstrated experience as a Data Engineer scaling Data heavy platforms Strong understanding of ETL pipelines and Data Architectures Python, SQL & Python libraries (Pandas & Spark) Modern Data Warehousing tools AWS/GCP experience Experience working in high-growth start-up or scale-up environments (essential) Hybrid working in Central London Pays £70k-£80k Unfortunately More ❯

AWS/GCP Key skills and requirements: Demonstrated experience as a Data Engineer scaling Data heavy platforms Strong understanding of ETL pipelines and Data Architectures Python, SQL & Python libraries (Pandas & Spark) Modern Data Warehousing tools AWS/GCP experience Experience working in high-growth start-up or scale-up environments (essential) Hybrid working in Central London Pays £100k-£120k Unfortunately More ❯

AWS/GCP Key skills and requirements: Demonstrated experience as a Data Engineer scaling Data heavy platforms Strong understanding of ETL pipelines and Data Architectures Python, SQL & Python libraries (Pandas & Spark) Modern Data Warehousing tools AWS/GCP experience Experience working in high-growth start-up or scale-up environments (essential) Hybrid working in Central London Pays £100k-£120k Unfortunately More ❯

engineering, mathematics, physics, statistics Experience of data science in finance, insurance or Ecommerce is an advantage but not required. Experience with neural networks and Tensor Flow, CatBoost, XGBoost, SKlearn, Pandas If you hold the experience and technical skills outlined above which would enable you to hit the ground running, please apply to find out more information about this exciting opportunity. More ❯

Data Scientist, your 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. More ❯

and have in-depth knowledge of how they work Python: You have built and deployed production-grade Python applications and you are familiar with data science libraries such as pandas and scikit-learn Tooling & Environment: ○ DevOps: You have experience working with DevOps tooling, such as gitops, Kubernetes, CI/CD tools (we use buildkite) and Docker Cloud: You have worked More ❯

or supply chain. Solid understanding of supervised learning, time-series forecasting, clustering , and other core ML techniques. Strong experience with SQL and querying large, complex datasets. Proficiency in Python (Pandas, scikit-learn, XGBoost, etc.); familiarity with R or dbt is a plus. Experience working with BI tools such as Looker, Tableau, or Power BI. Comfort working in high-growth environments More ❯

data analysis, statistics, and machine learning concepts. Proficiency in programming languages such as Python. Experience with data visualization tools like Power BI. Familiarity with data manipulation libraries such as Pandas and NumPy. Knowledge of machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn. Excellent problem-solving skills and attention to detail. Ability to work effectively both independently and as part More ❯