82 Data Science jobs in Cambridge
Data Science Intern - Analytics
Posted today
Job Viewed
Job Description
As a Data Science Intern, you will work closely with our experienced data science team on real-world projects, applying theoretical knowledge to practical challenges. Your responsibilities will include data cleaning and preprocessing, exploratory data analysis, feature engineering, and assisting in the development and evaluation of machine learning models. You will gain exposure to various data science tools and techniques, including programming languages like Python or R, and relevant libraries (e.g., Pandas, NumPy, Scikit-learn). This internship is an excellent chance to contribute to impactful research and development initiatives, learning best practices in data-driven decision-making. You will have the opportunity to present your findings and insights to the team, developing your communication and presentation skills. The ideal candidate will be a current undergraduate or postgraduate student pursuing a degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field. A strong analytical mindset, a passion for data, and foundational knowledge of statistics and programming are essential. While formal experience is not required, a demonstrable interest in data science through personal projects or coursework is highly valued. This fully remote internship provides a unique learning experience and the chance to build a strong foundation for a career in data science. We are looking for enthusiastic individuals who are eager to learn, contribute, and grow within a supportive and innovative environment.
Key Responsibilities:
- Assist in data cleaning, preprocessing, and transformation tasks.
- Perform exploratory data analysis to uncover insights and patterns.
- Support the development and evaluation of machine learning models.
- Conduct statistical analysis and hypothesis testing.
- Contribute to data visualization and reporting efforts.
- Collaborate with the data science team on ongoing research projects.
- Learn and apply various data science tools and techniques.
- Assist in documenting code and methodologies.
- Present findings and insights to the team.
- Engage actively in learning and professional development.
Qualifications:
- Currently pursuing a Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
- Foundational knowledge of statistical concepts and methods.
- Familiarity with programming languages like Python or R, and relevant data science libraries.
- Strong analytical and problem-solving abilities.
- Eagerness to learn and adapt to new technologies and methodologies.
- Excellent communication and interpersonal skills.
- Ability to work independently and collaboratively in a remote setting.
- Demonstrated interest in data science through coursework or projects.
- Proactive attitude and a strong work ethic.
- Must be eligible to undertake an internship.
Remote Data Science Apprentice
Posted 8 days ago
Job Viewed
Job Description
- Assisting senior data scientists with data collection, cleaning, and preparation.
- Performing exploratory data analysis to identify trends and patterns.
- Supporting the development and implementation of machine learning models.
- Contributing to data visualization projects to communicate insights.
- Learning and applying statistical modeling techniques.
- Documenting methodologies and findings clearly and concisely.
- Participating in team meetings and contributing to project discussions.
- Engaging actively in online learning modules and training sessions.
- Troubleshooting data-related issues under supervision.
- Adhering to data privacy and security protocols.
Qualifications:
- A strong passion for data and a desire to build a career in Data Science.
- Good analytical and problem-solving skills.
- Proficiency in mathematics and statistics.
- Basic understanding of programming concepts (e.g., Python, R) is a plus, but not essential.
- Excellent communication and collaboration skills, suitable for a remote team environment.
- Ability to learn quickly and adapt to new technologies.
- A minimum of A-Levels or equivalent in a relevant subject (e.g., Maths, Science, Computer Science) or equivalent work experience.
- Must be eligible for an apprenticeship program.
This remote apprenticeship provides a unique pathway into the tech industry, offering excellent career prospects upon successful completion.
Data Science Graduate Researcher
Posted 9 days ago
Job Viewed
Job Description
Data Science Graduate Programme
Posted 11 days ago
Job Viewed
Job Description
Responsibilities:
- Collaborate with senior data scientists and engineers to develop and implement machine learning models.
- Clean, process, and analyse large datasets to extract actionable insights.
- Design and execute experiments to test hypotheses and validate model performance.
- Develop data visualisations and reports to communicate findings to stakeholders.
- Stay abreast of the latest advancements in data science, AI, and machine learning techniques.
- Contribute to the development of new algorithms and methodologies.
- Participate in team meetings, offering innovative ideas and solutions.
- Document code, processes, and findings thoroughly.
Qualifications:
- Recent graduate (within the last two years) with a degree in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
- Strong foundation in statistical analysis and machine learning concepts.
- Proficiency in programming languages such as Python or R, including relevant libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow).
- Familiarity with SQL and database management.
- Excellent problem-solving and analytical skills.
- Strong communication and collaboration abilities, essential for a remote-first environment.
- A proactive attitude and a passion for data-driven decision-making.
- Previous experience with data analysis projects, internships, or relevant coursework is a plus.
This is a unique opportunity to gain hands-on experience in a dynamic and supportive remote environment, working on challenging problems that shape the future of technology. The role is based remotely, offering flexibility and a work-life balance that complements your academic and professional aspirations. We are committed to fostering a diverse and inclusive workplace where all graduates can thrive and contribute their unique perspectives. Join us and become part of a team that values innovation, continuous learning, and making a real-world difference in the world of data science, from anywhere in the Cambridge, Cambridgeshire, UK region.
Senior Research Data Engineer: MSR AI for Science
Posted 5 days ago
Job Viewed
Job Description
We seek a highly motivated Senior RSDE to join our Biomolecular Emulator (BioEmu) team. The BioEmu project aims to model the dynamics and function of proteins - how they change shape, bind to each other, and bind small molecules. This approach will help us to understand biological function and dysfunction on a structural level and lead to more effective and targeted drug discovery. Our BioEmu-1 model was published in Science ( (see our blog post ( for links to our open-source software and other resources and this explainer video ( ).
**Responsibilities**
+ Data integration for structure & dynamics: Build ingestion/curation pipelines for structural/biophysical data (mmCIF/PDB, EM maps/particles, binding/biophysics, spectroscopy); implement map/volume preprocessing (e.g., resolution filtering, normalization) and alignment to model inputs/outputs.
+ Cryo‑EM expertise: Operationalize end‑to‑end flows from raw image stacks/particles to 3D maps and model‑ready tensors; interoperate with community formats (e.g., EMDB/EMPIAR, mmCIF) and link to sequences/annotations.
+ Signal & information content: Design dataset diagnostics (e.g., mutual‑information‑like measures, effective sample size, SNR proxies) to quantify what data teach the model; build active‑learning loops that maximize learning per euro of data collection time.
+ Model‑aware data services: Implement scalable, versioned data services and feature stores that feed training/evaluation; design loaders/augmentations optimized for throughput and correctness (GPU‑aware).
+ Training‑at‑scale engineering: Own distributed data pipelines and orchestration for large runs on Azure; profile and tune I/O, storage tiers, data locality, and caching; monitor cost, utilization, and failure modes.
+ Quality, governance, and reproducibility: Codify schemas/ontologies, metadata contracts, unit/integration tests, and lineage; automate validation and data drift detection; maintain documentation and examples.
+ Partner across disciplines: Work closely with ML researchers, structural biologists, and drug designers; translate experimental constraints into robust computational workflows; communicate clearly and proactively.
**Qualifications**
Required:
+ PhD or equivalent experience in Computer Science, Machine Learning, Applied Mathematics, Computational Biology, or related field.
+ Strong software engineering in Python (packaging, testing, CI), with systems thinking for data‑intensive ML.
+ Deep learning experience (PyTorch/JAX/TensorFlow) and solid foundations in linear algebra, probability, and statistics.
+ Proven experience designing robust data pipelines for large‑scale ML (HPC or cloud).
+ Ability to reason about learning signal and to assess information content of real‑world scientific datasets.
+ Excellent collaboration and communication in interdisciplinary teams.
Preferred:
+ Hands‑on cryo‑EM experience (e.g., map reconstruction, refinement, or pipeline tooling).
+ CUDA or C++ for performance‑critical components; experience with mixed precision and memory‑efficient training.
+ Experience integrating experimental data into ML models (e.g., constraints/priors from cryo‑EM, binding assays, spectroscopy).
+ Familiarity with MD data, structure prediction systems, or protein design work-flows.
+ Experience with cost‑optimization for data collection and cloud utilization; clear track record of building reliable, maintainable research software at scale.
+ Experience with structural biology or molecular biology data/techniques (e.g., cryo‑EM, binding assays, spectroscopy, expression, sequencing)
#Research #AI for Science
In line with our Flexible Work approach, for roles at Microsoft Germany, we recommend spending at least two days per week in the office or at the customer site. The actual number of days to work from the office or at the customer will be defined in agreement between the employee and their manager.
Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations ( .
Senior Agronomist - Remote Sensing & Data Analysis
Posted 5 days ago
Job Viewed
Job Description
As a Senior Agronomist, you will leverage your expertise in geospatial technologies, satellite imagery, drone data, and advanced analytical techniques to provide actionable insights to our clients. Your work will directly contribute to enhancing precision agriculture practices, improving resource management, and supporting sustainable farming initiatives. This role requires a blend of scientific knowledge in agronomy and strong technical proficiency in data processing and interpretation. You will be expected to work independently, manage projects effectively, and communicate complex findings clearly to both technical and non-technical stakeholders.
Key Responsibilities:
- Utilize remote sensing data (satellite, aerial, drone) to monitor crop health, growth, and identify potential issues.
- Develop and implement algorithms and models for analyzing agricultural data, including spectral indices, yield prediction, and soil analysis.
- Interpret complex datasets to provide strategic recommendations for crop management, fertilization, irrigation, and pest control.
- Collaborate with software developers and data scientists to enhance data processing pipelines and analytical tools.
- Conduct field validation studies and ground-truthing to ensure data accuracy and model reliability.
- Communicate findings and recommendations through comprehensive reports, presentations, and dashboards.
- Support the development and deployment of new precision agriculture technologies and services.
- Stay abreast of the latest advancements in remote sensing, agronomy, and agricultural data science.
- Provide technical guidance and mentorship to junior team members.
- Contribute to research and development projects aimed at improving agricultural efficiency and sustainability.
Qualifications and Skills:
- Master's or Ph.D. in Agronomy, Crop Science, Agricultural Engineering, or a closely related field.
- Minimum of 5 years of experience in agronomy, with a significant focus on remote sensing and geospatial data analysis.
- Proficiency in using GIS software (e.g., ArcGIS, QGIS) and remote sensing platforms (e.g., ERDAS Imagine, ENVI).
- Strong programming skills in languages such as Python or R for data analysis and statistical modeling.
- Experience with satellite imagery processing and analysis (e.g., Sentinel, Landsat).
- Familiarity with agricultural sensors and data acquisition techniques.
- Excellent analytical, problem-solving, and critical thinking skills.
- Strong written and verbal communication skills, with the ability to explain technical concepts clearly.
- Proven ability to work independently and manage multiple projects in a remote setting.
- Passion for sustainable agriculture and innovative farming practices.
If you are a dedicated agronomist passionate about leveraging technology to transform agriculture and thrive in a remote work environment, we invite you to apply for this exciting opportunity.
Machine Learning Engineer
Posted 7 days ago
Job Viewed
Job Description
We built Neutreeno to transform business at speed and scale by revolutionising decarbonisation. Our innovative software, backed by cutting-edge science from the University of Cambridge, transforms how companies understand and reduce their emissions. By leveraging decades of research in resource and emissions flow data mapping, we identify the most impactful areas for emissions reduction, enabling companies to make strategic, data-driven decisions.
Working alongside leading sustainability scientists from the IPCC and IEA who shape international climate policy, we're creating a new paradigm in decarbonisation technology. Our approach has already been adopted by the world's largest companies. With our recent $5M seed round led by US investors, and coverage in ESGPost, YahooFinance, BusinessWeekly, and a nomination by Cambridge Institute for Sustainability Leadership for the 2025 Earthshot Prize, we're ready to grow.
Join us in revolutionising decarbonisation and tapping into the $130B addressable market!
As a Machine Learning Engineer at Neutreeno, you'll lead the development of intelligent systems that power our emissions and decarbonisation capabilities. Working at the cutting edge of machine learning and sustainability, you'll build platforms that transform complex data into actionable insights for thousands of companies globally. Your innovations will directly power breakthrough solutions that identify critical decarbonisation opportunities across entire industries and supply chains. You'll push the boundaries of what's possible with AI-driven climate tech developing advanced models that process vast amounts of unstructured data to reveal hidden patterns in global emissions. You'll collaborate with our climate science team to translate cutting-edge research into production-ready ML solutions, leveraging our graph-structured emissions models to understand complex supply chain relationships. You'll engage with technical stakeholders across industries, building scalable ML systems that drive decarbonisation at global scale.
- Use and fine-tune out-of-the-box models for matching textual and quantitative data to entries in our emissions database with uncertainty estimation
- Develop our emission intensity inference model using deep learning techniques that leverage uncertainty
- Advise on the out-of-the-box Large Language Models to extract data from a variety of sources
- Advise on construction of data-pipelines to integrate industrial and economic data into our database and to generate training data for our models
- Collaborate with climate mitigation scientists and process engineers to translate domain expertise into scalable ML solutions
- Evaluate and recommend appropriate ML implementations by balancing upfront setup costs and effort against business value and company goals
- Guide the integration of out-of-the-box AI tools to enhance internal operations and support front-end use cases
- Stay up-to-date with the latest ML theory, techniques, and out-of-the-box tools
- Contribute to technical documentation and present ML methodology insights to both internal teams and external stakeholders
- Master's or PhD in Computer Science, Machine Learning, Data Science, or related field
- Good foundational understanding and subsequent practical application of ML techniques, preferably NLPs, LLMs, Bayesian Optimisation and MCMCs
- Strong proficiency in Python and ML frameworks and tools (e.g. PyTorch, TensorFlow, JAX, Hugging Face, vLLM, MCP, prompt engineering)
- Excellent communication skills and ability to explain ML concepts to non-technical stakeholders
- Ability to work effectively in multi-disciplinary teams, collaborating across engineering, product, and commercial functions to translate requirements into integrated technical solutions
- Experience working on large codebase with multiple collaborators via version control tools (i.e. git, GitHub etc.)
- Experience with data scrapping (e.g. Beautiful Soup), extraction (e.g. LLMs) and cleaning.
- Knowledge with uncertainty quantification and probabilistic modelling approaches such as Bayesian optimisation
- Experience with cloud deployment pipelines (Docker, cloud platforms, AWS, CI/CD)
- Familiarity with sustainability or environmental data standards and frameworks
- Opportunity to make a significant impact on global decarbonisation efforts
- Join a collaborative and innovative work environment at the Cambridge Institute for Sustainability Leadership. Network with leading startups and industry climate professionals.
- Learning from world-leading mitigation climate scientists
- A hybrid work model (three days a week in our Cambridge office) that fosters both teamwork and individual flexibility
- £60,000 – 85,000 base DOE
- Stock option plan
- Incredible professional development and growth opportunities as we grow quickly
- Company pension
- Leading private health insurance
- Great on-site amenities (unlimited coffee, weekly events with global sustainability leaders.)
- Work from anywhere for up to two weeks per year
If you're passionate about leveraging your machine learning skills to make a tangible impact on global decarbonisation efforts, we want to hear from you!
Neutreeno is an equal opportunity employer committed to creating an inclusive workplace.
Be The First To Know
About the latest Data science Jobs in Cambridge !
Machine Learning Engineer
Posted 7 days ago
Job Viewed
Job Description
Machine Learning Engineer – On-Device Health Monitoring
Cambridge (1 day a week)
Up to £80,000 + Equity + Benefits
About the Role
We’re working with a pioneering health-tech start-up that’s transforming the way we measure human health through sound. Their mission is to create the world’s leading foundation model for turning sound into health insights — enabling preventative health monitoring through devices people already own.
They’re now looking for a Machine Learning Engineer to build and optimise on-device ML models for health and biosignal monitoring, helping take their technology from proof of concept to a production-ready product.
You’ll be at the forefront of developing models that run efficiently on constrained devices, working closely with the CTO on design, optimisation, and deployment. This is a hands-on technical role that offers full exposure to the early-stage startup experience — from prototyping and experimentation to strategic product decisions.
Key Responsibilities
- Develop, optimise, and deploy machine learning models for on-device health monitoring.
- Experiment with architectures and apply techniques such as quantisation, pruning, and compression to improve efficiency.
- Collaborate with cross-functional teams to take research prototypes into production-ready systems.
- Contribute to broader technical and product discussions, including data collection, validation, and feature development.
- Take ownership of projects, working autonomously while supporting the wider engineering team.
What We’re Looking For
- Ph.D. or Master’s degree in Computer Science, Machine Learning, Information or Biomedical Engineering (or similar).
- Strong experience with deep learning frameworks (PyTorch/TensorFlow) and Python development.
- Proven background in on-device ML (TinyML) using frameworks such as TensorFlow Lite, ExecuTorch, or TVM.
- Solid understanding of model optimisation for constrained hardware environments.
- Ability to write clean, maintainable, and well-tested code in a collaborative setting.
- Curiosity, adaptability, and enthusiasm for working in a fast-paced, early-stage environment.
- Experience working with time-series data such as audio or biosignals.
- Background in biomedical or signal processing.
- Experience writing production-level code or integrating models with embedded systems.
- Previous startup experience or exposure to medical device development.
Machine Learning Engineer
Posted 7 days ago
Job Viewed
Job Description
Machine Learning Engineer
UK – travel to Cambridge
£80,000-£85,000
We're working with an early-stage, deep tech start-up led by PhD-level founders with a strong background in audio-focused machine learning. Backed by solid technical vision and real-world applications, they’re developing ground-breaking software that transforms earbuds into health sensors, powered entirely by ML.
Now, they’re making their first technical hire.
What You’ll Do:
- Take ML models already in development and optimize them for on-device performance
- Compress and adapt models to run efficiently in varied environments
- Collaborate closely with a small, highly technical founding team
- Contribute to long-term technical strategy in a greenfield product space
What They’re Looking For:
- Strong industry experience
- Proven experience in on-device ML (beyond image/video)
- Ideally, exposure to bio-signals such as audio or PPG
- Proficient in Python
- Comfortable working in a fast-paced, ambiguous start-up environment
If you’re interested in making an application please share your CV with Rosie O’Callaghan or click on the link to apply.
Machine Learning Engineer
Posted today
Job Viewed
Job Description
Machine Learning Engineer
We are working with a new start up company who are developing state-of-the-art medical devices to monitor health systems.
Experience Required:
MSc or PhD in ML, Biomedical Engineering, or related field
Strong experience with PyTorch/TensorFlow and Python
Hands-on experience with TinyML or edge ML frameworks (TFLite, TVM, etc.)
Skilled in model optimization for constrained devices
Comfortable in a startup environment - proactive, collaborative, and adaptable
What you'll do:
Build and optimize deep learning models for on-device biosignal monitoring
Work on model compression, quantization, and other techniques for edge deployment
Collaborate closely with the CTO and technical team to deploy models in production
Contribute across the stack - from signal processing to product and hardware integration
Help shape the direction of a fast-moving startup
On Device, TinyML, Optimisation, Audio