82 Data Science jobs in Cambridge

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

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.

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

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

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