1 to 25 of 106 Pandas Jobs in the Thames Valley

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 More ❯

Desired Skills and Experience: Kafka and message bus/queue expertise Terraform and GitHub Actions skills Open Telemetry (OTEL) implementation Proficiency with NumPy and Pandas Systems integration experience Background in commodity trading (gas & power) Quantitative finance knowledge Understanding of compliance and regulation, particularly Sarbanes-Oxley (SOx) Role Responsibilities: Collaborate with More ❯

or senior role. Proven experience in a startup or fast-paced environment. Strong proficiency in Python, SQL, and data science libraries (e.g., scikit-learn, pandas, TensorFlow/PyTorch). Experience working with financial data, risk modelling, or algorithmic trading is a plus. Familiarity with cloud platforms (AWS, GCP, or Azure More ❯

big data tech Docker: both building but running too Wide AWS and infrastructure knowledge, including production support Scientific computing e.g. Numpy/scipy/pandas Just state the word 'Salmon' anywhere in your application, just to prove you can read a job advert. We aim to improve all our colleagues More ❯

ability to collaborate with both technical and non-technical teams This role is 70% hands on, 30% management Technical Expertise Required - Python - proficiency with Pandas, Numpy, SQLAlchemy, FastAPI, and Pydantic MySQL, PostgreSQL, and Redis Hands-on experience with Google Cloud Platform (GCP) services like Pub/Sub, Cloud Run, VMs More ❯

and business intelligence A good understanding of Python, including fundamental knowledge and a willingness to learn, along with familiarity with packages such as NumPy, pandas, and scikit-learn. Proficiency in SQL is required, along with experience in Unix and familiarity with scheduling tools like Tidal and Airflow. Good understanding of More ❯

hands-on development experience in Python or another object-oriented programming language. Strong proficiency in Python’s data ecosystem, including libraries such as NumPy , Pandas , and Matplotlib . Experience working with cloud platforms (AWS preferred) and Infrastructure-as-Code tools. Solid understanding of relational databases and SQL. Proven track record More ❯

with retrieval-augmented generation (RAG) systems Knowledge of optimization techniques for reducing LLM latency and computational costs Software Engineering & Architecture Strong Python development skills (pandas, numpy, scikit-learn, PyTorch/TensorFlow) Experience with web frameworks (Streamlit, FastAPI, or Flask) and frontend technologies Database design and optimization (PostgreSQL preferred) for large More ❯

with experience in backend frameworks like Flask , FastAPI , or Django . Ability to write efficient, reusable, and scalable code. Experience with libraries like NumPy , Pandas , SciPy , or any others commonly used in data processing or modelling. Strong understanding of data structures , algorithms , and complexity analysis . Mathematical or Computational Modelling More ❯

Frameworks : PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data More ❯

including 3+ years of Python in production (e.g., Flask, Django, Gunicorn, SQLAlchemy, Psycopg2). Strong background in working with large datasets using tools like Pandas, NumPy, or Spark. At least 2 years' experience with machine learning, deep learning, or AI systems. Familiarity with key libraries and frameworks such as TensorFlow More ❯

a revelant role. This will mean exposure to market data vendors, ability to communicate with traders and mentoring junior engineers. Tech stack: Deep Python, Pandas, AWS, Airflow, Kubernetes, ETL, SQL Please apply if this of interest More ❯

with 5+ years working in Python or another object-oriented environment. Strong familiarity with tools and libraries for data processing and visualization (e.g., NumPy, Pandas, Matplotlib). Comfortable deploying applications in cloud ecosystems, particularly AWS, and using infrastructure automation tools. Experienced with building data workflows and managing databases; Airflow and More ❯

Grafana, or Prometheus. Strong track record designing high-availability workflows with strict SLOs (>99%) using orchestration tools like Airflow. Expert-level proficiency in Python, Pandas, and SQL, with experience working on platforms like Snowflake and Postgres. Excellent communication and collaboration skills, with the ability to work autonomously in a distributed More ❯

or similar. Knowledge of data science concepts: data preparation, exploration, modeling, applicable to structured or unstructured data. Experience with data science tools like Jupyter, pandas, matplotlib, seaborn, numpy. Experience with API testing and security tools such as Postman, Burp Suite, OWASP ZAP. Knowledge of database management systems like MySQL. Hands More ❯

years of experience in a quantitative, analytics, or developer role within a financial institution or trading environment. Strong proficiency in Python (e.g., Pandas, NumPy, Jupyter) and experience building data pipelines , analytical tools , or dashboards . SQL experience is a plus. Proficiency in Excel and data visualization platforms such as Power More ❯

years of experience in a quantitative, analytics, or developer role within a financial institution or trading environment. Strong proficiency in Python (e.g., Pandas, NumPy, Jupyter) and experience building data pipelines , analytical tools , or dashboards . SQL experience is a plus. Proficiency in Excel and data visualization platforms such as Power More ❯

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 More ❯

and the ability to apply this process when handling structured or unstructured data Confident with using common data science tooling such as Jupyter notebooks, pandas, matplotlib, seaborn, numpy API testing and security tools: Postman, Burp Suite, OWASP ZAP, etc. Strong knowledge of database management systems (DBMS) such as MySQL Hands More ❯

and the ability to apply this process when handling structured or unstructured data Confident with using common data science tooling such as Jupyter notebooks, pandas, matplotlib, seaborn, numpy API testing and security tools: Postman, Burp Suite, OWASP ZAP, etc. Strong knowledge of database management systems (DBMS) such as MySQL Hands More ❯

and the ability to apply this process when handling structured or unstructured data Confident with using common data science tooling such as Jupyter notebooks, pandas, matplotlib, seaborn, numpy API testing and security tools: Postman, Burp Suite, OWASP ZAP, etc. Strong knowledge of database management systems (DBMS) such as MySQL Hands More ❯

and the ability to apply this process when handling structured or unstructured data Confident with using common data science tooling such as Jupyter notebooks, pandas, matplotlib, seaborn, numpy API testing and security tools: Postman, Burp Suite, OWASP ZAP, etc. Strong knowledge of database management systems (DBMS) such as MySQL Hands More ❯

with exposure to capital markets or trading (equities and/or execution). Technical Skills: Proficient in Python with some exposure to data analysis (Pandas, NumPy) or similar (R, MATLAB). Solid background in object-oriented programming (Java preferred; C++ or C# also valued). Familiarity with Linux-based systems More ❯

and the ability to apply this process when handling structured or unstructured data Confident with using common data science tooling such as Jupyter notebooks, pandas, matplotlib, seaborn, numpy API testing and security tools: Postman, Burp Suite, OWASP ZAP, etc. Strong knowledge of database management systems (DBMS) such as MySQL Hands More ❯

and machine learning, with a strong portfolio of relevant projects. Proficiency in Python with libraries like TensorFlow, PyTorch, or Scikit-learn for ML, and Pandas, PySpark, or similar for data processing. Experience designing and orchestrating data pipelines with tools like Apache Airflow, Spark, or Kafka. Strong understanding of SQL, NoSQL More ❯