407 Junior Data Scientists jobs in the United Kingdom

The Administrative Support Officer provides essential support to ensure efficient office operations and assists various departments by managing administrative tasks, coordinating communication, and maintaining organizational systems. This role is vital in fostering a productive and organized work environment.

Key Responsibilities

• Manage day-to-day office activities including scheduling, correspondence, and filing.
• Handle incoming calls, emails, and visitor inquiries professionally and promptly.
• Prepare and process documents, reports, and presentations as required.
• Maintain office supplies inventory and coordinate procurement when necessary.
• Assist in organizing meetings, events, and travel arrangements.
• Support HR and finance departments with data entry and record-keeping tasks.
• Ensure office equipment is functioning and arrange for repairs when needed.
• Maintain accurate and confidential records and databases.

Qualifications & Skills

• High school diploma or equivalent; relevant certifications or diploma in office administration is an advantage.
• Proven experience in administrative or office support roles.
• Excellent organizational and multitasking skills.
• Strong communication and interpersonal abilities.
• Proficiency in Microsoft Office Suite (Word, Excel, Outlook).
• Ability to handle sensitive information with discretion.
• Detail-oriented with good problem-solving skills.

• Develop, implement, and deploy advanced machine learning models for various business applications, including predictive analytics, classification, and clustering.
• Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies in large datasets.
• Clean, transform, and preprocess data from diverse sources to prepare it for analysis.
• Evaluate and optimize model performance using appropriate metrics and validation techniques.
• Communicate complex findings and insights clearly to both technical and non-technical stakeholders through visualizations and reports.
• Collaborate with software engineers and product managers to integrate ML models into production systems.
• Stay abreast of the latest advancements in machine learning, artificial intelligence, and data science.
• Mentor junior data scientists and contribute to the team's technical growth.
• Design and conduct A/B tests and other experiments to measure the impact of model deployments.
• Contribute to the development of the company's data strategy and best practices.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
• Minimum of 5 years of experience as a Data Scientist or in a similar role, with a strong focus on machine learning.
• Proficiency in programming languages commonly used in data science, such as Python (with libraries like scikit-learn, TensorFlow, PyTorch, Pandas, NumPy) and R.
• Extensive experience with SQL for data extraction and manipulation.
• Strong understanding of various machine learning algorithms (e.g., regression, decision trees, SVMs, neural networks) and their applications.
• Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation skills, with the ability to explain technical concepts to a non-technical audience.
• Demonstrated ability to work independently and manage multiple projects in a remote environment.
• Experience with cloud platforms (AWS, Azure, GCP) and MLOps is highly desirable.

• Design, develop, and implement advanced machine learning models and algorithms to solve complex business problems.
• Extract, clean, and preprocess large datasets from various sources.
• Perform exploratory data analysis to identify patterns, trends, and opportunities.
• Build and deploy scalable machine learning pipelines for production environments.
• Evaluate model performance, iterate on algorithms, and optimize solutions.
• Collaborate with software engineers to integrate ML models into existing products and platforms.
• Communicate complex findings and insights clearly to both technical and non-technical stakeholders through visualizations and presentations.
• Mentor junior data scientists and contribute to the team's technical growth.
• Stay current with the latest advancements in machine learning, AI, and data science research.
• Develop and maintain robust documentation for models, methodologies, and code.
• Identify new data sources and opportunities for enhancing analytical capabilities.
• Contribute to the company's data strategy and roadmap.
• Participate in code reviews and knowledge sharing sessions.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
• 5+ years of experience in data science, with a strong focus on machine learning model development and deployment.
• Proficiency in programming languages such as Python or R, and relevant libraries (e.g., Scikit-learn, TensorFlow, PyTorch, Keras).
• Strong understanding of statistical modeling, data mining techniques, and machine learning algorithms (e.g., regression, classification, clustering, deep learning).
• Experience with big data technologies (e.g., Spark, Hadoop) and SQL.
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and MLOps practices.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation skills, with the ability to explain technical concepts to a diverse audience.
• Experience in a specific domain relevant to the company's industry is a plus.

• Collaborate with senior data scientists and clients to understand business needs and translate them into data science problems.
• Collect, clean, and pre-process large datasets from various sources.
• Design, develop, train, and evaluate machine learning models using Python, R, and relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Implement and deploy machine learning solutions into production environments.
• Perform exploratory data analysis to uncover insights and identify patterns.
• Develop data visualizations and dashboards to communicate findings effectively to technical and non-technical stakeholders.
• Stay up-to-date with the latest advancements in machine learning, deep learning, and AI research.
• Contribute to the documentation of data science processes, methodologies, and model performance.
• Participate in code reviews and team discussions to ensure code quality and knowledge sharing.
• Assist in the preparation of client presentations and project proposals.

• A recent graduate (2023 or 2024) with a degree (2:1 or above) in Computer Science, Data Science, Statistics, Mathematics, Physics, or a related quantitative field.
• Strong foundation in machine learning algorithms, statistical modelling, and data mining techniques.
• Proficiency in Python programming, including libraries such as Pandas, NumPy, and scikit-learn.
• Familiarity with machine learning frameworks like TensorFlow or PyTorch is a plus.
• Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau) is advantageous.
• Excellent analytical, problem-solving, and critical thinking abilities.
• Strong communication and collaboration skills, with the ability to explain complex concepts clearly.
• Demonstrated passion for data science and machine learning, evidenced through personal projects, Kaggle competitions, or academic research.
• Ability to work independently, manage time effectively, and meet deadlines in a remote setting.
• A proactive attitude towards learning and continuous development.

• Assist in collecting, cleaning, and preparing large datasets for analysis.
• Develop and implement machine learning models for predictive analytics and insights.
• Conduct exploratory data analysis to identify trends and patterns.
• Collaborate with senior data scientists on model evaluation and validation.
• Create data visualizations to communicate findings effectively.
• Participate in code reviews and contribute to the development of data science pipelines.
• Assist in deploying machine learning models into production environments.
• Stay current with advancements in data science and machine learning techniques.
• Document methodologies, processes, and results clearly.
• Support business units by providing data-driven insights and recommendations.

• Recent graduate with a degree in Computer Science, Statistics, Mathematics, Physics, Engineering, or a related quantitative field.
• Strong understanding of statistical concepts and machine learning algorithms.
• Proficiency in Python or R, including relevant data science libraries (e.g., Pandas, NumPy, Scikit-learn).
• Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Tableau).
• Basic knowledge of SQL and database querying.
• Excellent analytical and problem-solving skills.
• Strong communication and interpersonal abilities.
• Eagerness to learn and adapt to new technologies.
• Ability to work independently and as part of a remote team.
• Enthusiasm for data science and its applications.

Role: Machine Learning Operations Engineer
Location: Oxfordshire
Salary: 65,000 - 75,000

This is an exciting opportunity to join a world leading company specialising in motion capture and tracking systems, with products used globally in the entertainment, engineering, and life sciences sectors. My Client are looking for a talented Machine Learning Operations Engineer to support and enhance their cutting edge machine learning capabilities.

The Role

You will join a collaborative Research and Development team based in Oxford, contributing to the development and maintenance of a modern ML operations stack. This includes data acquisition pipelines, data management, and machine learning model training infrastructure. The environment includes both self-managed on premise systems and cloud-based infrastructure, primarily using AWS.

You will have the opportunity to influence the technical direction of the ML Ops team, propose new areas for development, and potentially lead your own projects.

This is a hybrid role combining remote and on site working. There is no expectation to be available outside core business hours.

Key Responsibilities

• Maintain and improve ML Ops infrastructure
• Manage on premise Kubernetes clusters and ML pipelines
• Integrate ML toolkits into operational workflows
• Collaborate with ML developers to streamline workflows
• Suggest and implement technical improvements and new tools

Required Skills and Experience

• Academic background (research Masters level) or industry experience in a relevant field
• Strong experience managing on premise Kubernetes clusters
• Deep knowledge of Kubeflow or similar systems such as MLflow
• Proficient in Python and experienced with Linux systems
• Familiar with AWS services such as Cognito, S3, EC2 and Lambda
• Experience working with ML frameworks such as PyTorch or Lightning
• Capable of designing and delivering ML Ops solutions across various platforms

Desirable Skills

• Background in DevOps with experience in CI systems such as Jenkins
• Familiarity with infrastructure as code tools such as Ansible
• Interest in human motion capture, sports tech, or animation
• Exposure to C++ is a bonus

Benefits

• 65,000 - 75,000 (DOE)
• 10 percent company pension
• 25 days annual leave plus bank holidays
• Life assurance
• Private medical insurance including dental and optical
• Permanent health insurance
• Cycle to work scheme
• Free on site parking

WR Engineering are the #1 recruitment partner for engineering, manufacturing & technical sales jobs. We recruit for permanent and contract jobs UK wide.

WR is acting as an Employment Agency in relation to this vacancy.

Job Description:

We are seeking an experienced Machine Learning Engineer with expertise in big programmes and has contributed to the delivery of complex business cloud solutions. The ideal candidate will have a strong background in Machine Learning engineering and an expert in operationalising models in the Databricks MLFlow environment (chosen MLOps Platform).

Responsibilities:

• Collaborate with Data Scientists and operationalize the model with auditing enabled, ensure the run can be reproduced if needed.
li>Implement Databricks best practices in building and maintaining economic modelling (Machine Learning) pipelines.
• Ensure the models are modular. Ensure the model is source-controlled with agreed release numbering.
• Extract any hard-coded elements and parameterise them so that the model execution can be controlled through input parameters.
• Ensure the model input parameters are version-controlled and logged to the model execution runs for auditability.
• Ensure model metrics are logged to the model runs.
• Ensure model logging, monitoring, and alerting to make sure any failure points are captured, monitored and alerted for support team to investigate or re-run of the model involves running of multiple experiments and choosing the best model (champion challenger) based on the accuracy/error rate of each experiment, ensure this is done in an automated manner.
li>Ensure the model is triggered to run as per the defined schedule. If the process involves executing multiple models feeding each other to produce the final business outcome, orchestrate them to run based on the defined dependencies.
• Define and Maintain the ML Frameworks (Python, R & MATLAB templates) with any common reusable code that might emerge as part of model developments/operationalisation for future
• models to benefit.
• Where applicable, capture data drift, concept drift, model performance degradation signals and ensure model retrain.
• Implement CI/CD pipelines for ML models and automate the deployment.
• Maintain relevant documentation.

Requirements:

• Bachelor's degree in a relevant field.
• Minimum of 5 years of experience as a business analyst, with a focus on capturing and documenting business requirements and business processes.
• Strong understanding of banking and financial industry practices and regulations.
• Solid knowledge of Data Management process, data analysis and modelling techniques.
• Experience in monetary policy analysis (nice to have)
• Experience in time series database analysis
• Familiarity with business intelligence tools and concepts.
• Strong analytical and problem-solving skills.
• Experience in managing software development lifecycles within Agile frameworks to ensure timely and high-quality delivery.
• Excellent communication and collaboration skills.
• Ability to adapt to changing requirements and priorities in a fast-paced environment.