644 Junior Data Scientists jobs in the United Kingdom

• Responsible for data analysis, entry and Validation related activities across the Pirbright manufacturing facility, to enable the large scale production of FMD and BTV antigen and finished product in a timely and cost effective manner.
• Analyse production data to identify trends, deviations, and performance metrics.
• Assist in the execution of cleaning validation protocols in line with GMP requirements.
• Bring in SQL / Power BI approaches to improve the site approach to data.
• Support sampling, documentation, and coordination of validation activities.

• GMP experience is essential
• Have a scientific or engineering discipline degree (e.g., Biochemistry, Biotechnology, Chemical Engineering, Pharmaceutical Sciences).
• Understanding of cleaning validation principles and documentation practices.
• Experience with data handling and analytics
• Knowledge of HSE, bio-safety standards, GMP regulations as well as main tools and principles of operational excellence

• Lead the design, development, implementation, and maintenance of scalable bioinformatics pipelines for next-generation sequencing (NGS) data analysis (e.g., WGS, WES, RNA-Seq).
• Analyse and interpret complex genomic and transcriptomic datasets to identify genetic variants, gene expression patterns, and other biologically relevant insights.
• Collaborate closely with biologists, geneticists, clinicians, and computational scientists to define project goals and analytical strategies.
• Develop and apply statistical and machine learning methods for genomic data analysis.
• Ensure the quality, reproducibility, and documentation of all bioinformatics analyses and workflows.
• Stay abreast of the latest advancements in bioinformatics, genomics, and related computational fields.
• Mentor and guide junior bioinformaticians and data analysts.
• Contribute to research proposals, grant applications, and publications.
• Manage data storage, processing, and computational resources effectively.
• Present findings to scientific teams and external collaborators.

• PhD in Bioinformatics, Computational Biology, Genetics, Computer Science, or a related quantitative field.
• Minimum of 6 years of post-doctoral or industry experience in bioinformatics, with a strong focus on genomics data analysis.
• Extensive experience with NGS data processing and analysis pipelines (e.g., BWA, GATK, STAR, Salmon).
• Proficiency in programming languages such as Python, R, and shell scripting.
• Strong knowledge of statistical methods and machine learning techniques applicable to genomics.
• Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and containerization (e.g., Docker, Singularity) is highly desirable.
• Proven ability to manage complex projects and lead a small team.
• Excellent problem-solving, analytical, and critical thinking skills.
• Strong communication and presentation skills, with a track record of publications.
• Ability to work independently and collaboratively in a remote research environment.

At Tata Technologies we make product development dreams a reality by designing, engineering and validating the products of tomorrow for the worlds leading manufacturers. Due to our continued growth we are now recruiting for a Data Analysis Engineer SOX Algorithm to be based at our customers site in Gaydon .

Our Embedded Systems Solutions department (ESS) encompasses all activities outlined under th.

WHJS1_UKTJ

Job Description CK Group are recruiting for a Data Analysis and Validation Associate to join a global animal health company on a contract basis for 12 months.

Salary:

£147.90 per day PAYE

Data Analysis and Validation Associate role:

Responsible for data analysis, entry and Validation related activities across the Pirbright manufacturing facility, to enable the large scale production of FMD and BTV antigen and finished product in a timely and cost effective manner.
Analyse production data to identify trends, deviations, and performance metrics.
Assist in the execution of cleaning validation protocols in line with GMP requirements.
Bring in SQL / Power BI approaches to improve the site approach to data.
Support sampling, documentation, and coordination of validation activities.

Your Background :

GMP experience is essential
Have a scientific or engineering discipline degree (e.g., Biochemistry, Biotechnology, Chemical Engineering, Pharmaceutical Sciences).
Understanding of cleaning validation principles and documentation practices.
Experience with data handling and analytics
Knowledge of HSE, bio-safety standards, GMP regulations as well as main tools and principles of operational excellence

Company:

Our client is a leading manufacturer of vaccines, parasiticides and pharmaceuticals to protect animals. They are also one of the fastest growing animal health companies in the UK.

Location:

This role is based at our clients site near Pirbright.

Apply:

For more information, or to apply for this Data Analysis and Validation Associate role, please contact the Key Accounts Team on or email Please quote reference 150 622.

It is essential that applicants hold entitlement to work in the UK

Please note: This role may be subject to a satisfactory basic Disclosure and Barring Service (DBS) check.

INDKA

• MSc or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
• Proven experience as a Data Scientist, with a strong portfolio of machine learning projects.
• Expertise in programming languages such as Python or R and ML libraries.
• Solid understanding of statistical concepts, ML algorithms, and data mining techniques.
• Experience with big data technologies and cloud computing platforms.
• Strong analytical, problem-solving, and communication skills.
• Ability to work effectively in a hybrid environment.
• Familiarity with data visualization tools.

• Design, develop, and deploy machine learning models to solve complex business problems in the construction industry, such as predictive maintenance, project risk assessment, and resource optimization.
• Conduct advanced data analysis, statistical modeling, and exploratory data analysis to identify trends and insights from large, complex datasets.
• Collaborate closely with cross-functional teams, including engineers, project managers, and business analysts, to understand requirements and translate them into data-driven solutions.
• Clean, process, and transform raw data into usable formats for model training and analysis.
• Evaluate and validate model performance, iterating on approaches to improve accuracy and robustness.
• Communicate complex findings and recommendations clearly and effectively to both technical and non-technical stakeholders through reports and presentations.
• Stay abreast of the latest advancements in machine learning, deep learning, and data science techniques and explore their application to our business.
• Contribute to the development of the company's data infrastructure and analytics capabilities.
• Mentor junior data scientists and contribute to the growth of the data science practice within the organization.

• Master's or PhD degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Minimum of 5 years of professional experience in data science, with a strong focus on machine learning and predictive modeling.
• Proficiency in programming languages such as Python or R, and associated data science libraries (e.g., Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy).
• Experience with SQL and working with relational databases.
• Solid understanding of statistical concepts, machine learning algorithms (e.g., regression, classification, clustering, deep learning), and model evaluation techniques.
• Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau).
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies is a plus.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation skills, with the ability to articulate technical concepts to diverse audiences.
• Ability to work collaboratively in a team environment and manage project timelines effectively.

• Design, develop, and implement sophisticated machine learning models and algorithms to address complex scientific challenges.
• Analyse and interpret large, complex datasets from various sources, identifying trends, patterns, and actionable insights.
• Develop and deploy robust data pipelines for data collection, cleaning, transformation, and feature engineering.
• Collaborate closely with researchers, engineers, and domain experts to understand project requirements and translate them into data science solutions.
• Evaluate and select appropriate statistical methods and machine learning techniques for specific problems.
• Develop metrics and frameworks to assess the performance and impact of deployed models.
• Communicate complex findings and recommendations clearly and effectively to both technical and non-technical audiences through reports and presentations.
• Stay up-to-date with the latest advancements in data science, machine learning, AI, and relevant scientific fields.
• Mentor junior data scientists and contribute to the growth of the data science team.
• Contribute to the overall research and development strategy, identifying new opportunities for data-driven innovation.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
• Minimum of 5 years of professional experience in data science and machine learning.
• Proven track record of developing and deploying production-level machine learning models.
• Proficiency in programming languages such as Python or R, and associated data science libraries (e.g., TensorFlow, PyTorch, scikit-learn, Pandas, NumPy).
• Strong understanding of statistical modelling, experimental design, and data mining techniques.
• Experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., AWS, Azure, GCP).
• Excellent problem-solving skills and the ability to think critically and creatively.
• Strong communication and collaboration skills, with the ability to explain technical concepts to diverse audiences.
• Experience in scientific research environments or specific scientific domains is a significant plus.
• Ability to work effectively in a hybrid model, with a strong self-management capability for remote work.

• Design, develop, and deploy machine learning models to solve business problems.
• Perform data mining, feature engineering, and data preprocessing.
• Evaluate and optimize model performance using various metrics.
• Implement and manage ML pipelines and workflows.
• Collaborate with engineers to integrate ML models into production systems.
• Conduct statistical analysis and hypothesis testing.
• Interpret and communicate complex findings to stakeholders.
• Stay abreast of the latest research and advancements in ML and AI.
• Contribute to the development of data science best practices and standards.
• Mentor junior data scientists and contribute to team growth.

• PhD or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
• 5+ years of experience in data science and machine learning.
• Proven track record of developing and deploying ML models in production.
• Expertise in Python or R, and proficiency with ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong understanding of statistical modeling, algorithms, and data mining techniques.
• Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong written and verbal communication skills.
• Ability to work independently and manage projects effectively in a remote setting.

• Design, develop, and deploy advanced machine learning models and algorithms to address research and development objectives.
• Analyse large and complex datasets from various scientific sources, identifying patterns, trends, and correlations.
• Clean, pre-process, and transform raw data into formats suitable for ML model training.
• Evaluate model performance, perform rigorous testing, and optimise models for accuracy, efficiency, and scalability.
• Collaborate with research scientists, engineers, and domain experts to understand project requirements and translate them into data science solutions.
• Communicate complex findings and model insights clearly and concisely to both technical and non-technical stakeholders through reports, presentations, and visualisations.
• Stay current with the latest advancements in machine learning, artificial intelligence, and data science research.
• Develop and maintain robust data pipelines and MLOps practices.
• Mentor junior data scientists and contribute to the overall knowledge sharing within the data science team.
• Identify opportunities for leveraging data science and machine learning to improve research processes and outcomes.
• Ensure ethical considerations and data privacy are integrated into all data science practices.

• A Master's or PhD degree in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
• A minimum of 5 years of hands-on experience in data science and machine learning, with a strong focus on applied research.
• Proven expertise in developing and implementing ML models using languages such as Python or R, and libraries like TensorFlow, PyTorch, Scikit-learn.
• Strong knowledge of statistical modelling, data mining, and data analysis techniques.
• Experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., AWS, Azure, GCP).
• Proficiency in SQL and experience with various database technologies.
• Excellent problem-solving, analytical, and critical thinking skills.
• Strong communication, presentation, and interpersonal skills.
• Ability to work effectively in a hybrid work environment and collaborate across teams.
• Experience in scientific research environments is highly advantageous.