1 to 25 of 36 Permanent SciPy Jobs

Enhance and create advanced data visualisation applications. Requirements: Proficient in Software Python Development. 3-5 years experience in software engineering Experience with libraries/frameworks such as Pandas, Numpy, Scipy, etc. Skilled in data pipeline orchestration management libraries (e.g., Airflow, Prefect). Experience with cloud infrastructure (AWS, GCP, Azure). DevOps skills (CI/CD, containerisation). Familiarity with Plotly More ❯

Enhance and create advanced data visualisation applications. Requirements: Proficient in Software Python Development. 3-5 years experience in software engineering Experience with libraries/frameworks such as Pandas, Numpy, Scipy, etc. Skilled in data pipeline orchestration management libraries (e.g., Airflow, Prefect). Experience with cloud infrastructure (AWS, GCP, Azure). DevOps skills (CI/CD, containerisation). Familiarity with Plotly More ❯

technology initiatives and business objectives. Required Qualifications: 6+ years of experience in full stack software development. Proficiency in sever side Python programming. Proficiency in data analysis using Pandas, Numpy, SciPy etc. Experience with object oriented design, distributed systems architecture, performance tuning. Experience with designing and programming relational database such as MySQL, RedShift, Oracle SQL Server, or Postgres. Experience with AWS More ❯

or related field. Deep understanding of option pricing theory (Black-Scholes, local/stochastic volatility, Monte Carlo). Expert Python developer with strong numerical and vectorized coding skills (NumPy, SciPy, Pandas). Experience building and calibrating volatility surfaces and handling risk measures (Greeks, VaR, sensitivities). Strong background in stochastic calculus, numerical methods, and optimization. More ❯

or related field. Deep understanding of option pricing theory (Black-Scholes, local/stochastic volatility, Monte Carlo). Expert Python developer with strong numerical and vectorized coding skills (NumPy, SciPy, Pandas). Experience building and calibrating volatility surfaces and handling risk measures (Greeks, VaR, sensitivities). Strong background in stochastic calculus, numerical methods, and optimization. More ❯

and performance tuning. Leveraging statistical methods, data analysis, and modern compute infrastructure to improve research and execution workflows. What you need Strong programming background, primarily in Python (NumPy, Pandas, SciPy, etc.). Solid understanding of computer science fundamentals – algorithms, data structures, and software design. Experience within a quant, trading, or financial markets environment (hedge fund, prop desk, or similar) is More ❯

and performance tuning. Leveraging statistical methods, data analysis, and modern compute infrastructure to improve research and execution workflows. What you need Strong programming background, primarily in Python (NumPy, Pandas, SciPy, etc.). Solid understanding of computer science fundamentals – algorithms, data structures, and software design. Experience within a quant, trading, or financial markets environment (hedge fund, prop desk, or similar) is More ❯

Mentor junior developers and foster a culture of technical excellence and collaboration. What We’re Looking For 5+ years’ experience in Python and its scientific libraries (e.g. pandas, NumPy, SciPy). Strong understanding of cloud infrastructure (AWS preferred; Azure/GCP also welcome). Proven experience in system design and data modelling for scalable applications. Solid grasp of relational databases More ❯

Mentor junior developers and foster a culture of technical excellence and collaboration. What We’re Looking For 5+ years’ experience in Python and its scientific libraries (e.g. pandas, NumPy, SciPy). Strong understanding of cloud infrastructure (AWS preferred; Azure/GCP also welcome). Proven experience in system design and data modelling for scalable applications. Solid grasp of relational databases More ❯

Technical Expertise: A deep command of Python, experience working closely with hardware plus proven experience developing scientific or analytical applications using numerical and engineering-oriented Python tools (e.g., NumPy, SciPy). Location: Cambridge Position: Senior Software Engineer Salary: Up to £100K More ❯

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 cross-functionally More ❯

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 cross-functionally More ❯

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 cross-functionally More ❯

learning Familiarity with supervised and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms (GCP, AWS, Azure) and tools like Dataiku and Databricks Experience with ML Ops, including deployment, monitoring, and retraining pipelines Ability to work More ❯

recommendation systems and deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. 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. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross More ❯

recommendation systems and deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. 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. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross More ❯

recommendation systems and deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. 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. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross More ❯

recommendation systems and deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. 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. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross More ❯

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 to work More ❯

tools such as Airflow, Prefect, or Temporal, or by building bespoke pipeline systems for multi-step autonomous processes. You bridge science and engineering — comfortable with scientific computing libraries (NumPy, SciPy, pandas) and familiar with scientific databases and literature formats. What Sets You Apart: You have a research background — perhaps as a former academic researcher or research software engineer in ML More ❯

tools such as Airflow, Prefect, or Temporal, or by building bespoke pipeline systems for multi-step autonomous processes. You bridge science and engineering — comfortable with scientific computing libraries (NumPy, SciPy, pandas) and familiar with scientific databases and literature formats. What Sets You Apart: You have a research background — perhaps as a former academic researcher or research software engineer in ML More ❯

adopting AI tooling. What You’ll Bring • 5+ years in software/ML engineering, ideally with production deployment experience. • Strong Python background, comfortable across data frameworks like Pandas, NumPy, SciPy, Dask, Polars, or PySpark. • Proven experience setting up ML pipelines, integrating with AWS/Databricks, and applying CI/CD principles. • Solid understanding of time-series forecasting and supervised ML More ❯

+ equivalent experience) in Computer Science, Data Science, Mathematics, Physics, or Bioinformatics 5+ years post-qualification experience tackling complex technical challenges Expert-level Python programming (NumPy, PyTorch/TensorFlow, SciPy, pandas) Deep expertise in time-series and sequence modelling (RNNs, LSTMs, transformers, attention mechanisms) Strong foundation in probabilistic models (HMMs, Bayesian inference, graphical models) Track record of delivering impactful research More ❯

+ equivalent experience) in Computer Science, Data Science, Mathematics, Physics, or Bioinformatics 5+ years post-qualification experience tackling complex technical challenges Expert-level Python programming (NumPy, PyTorch/TensorFlow, SciPy, pandas) Deep expertise in time-series and sequence modelling (RNNs, LSTMs, transformers, attention mechanisms) Strong foundation in probabilistic models (HMMs, Bayesian inference, graphical models) Track record of delivering impactful research More ❯

Providing relevant insights Advanced statistical and analytical techniques and concepts sampling methods, Regression Properties of distributions Weighting sample-based data Statistical tests proper usage Real-world applications. Python - NumPy, SciPy, Pandas, MLlib, scikit-learn, and other common data and machine learning related libraries Working knowledge of SQL, data structures and databases (Snowflake - desirable) This is a pragmatic and humble organisation More ❯