What Jobs are available for Data Analytics in Bristol?

 
The Role: Data Analytics Consultant
Location: Bristol
Hybrid Working : Yes

Life In the Experience Delivery team

In the Experience Delivery team we look after large scale end to end UX, Design and build projects. Our team is made up of creative technologists, UX Designers and Strategic UX leaders. We work with the wider Nomensa Studio team to provide leadership and guidance to large multidisciplinary teams as they take clients problems from research and problem framing through to cutting edge design and technology implementations. Both strategic and creative thinking are at the centre of everything we do whilst we also keep an eye on delivering quality for our clients.

The team is made up of a Head of Experience Delivery with a layer of Principal consultants leading and mentoring senior and junior team members. The team have regular practice groups and surgery sessions to collaborate and learn from one another as well as training and inspirational sessions covering disciplines across the range of UX, Design, SEO and Tech. SEO is at the heart of this process as we strive to deliver exceptional experiences that work for all users.

We're advertising two roles within this space, SEO Consultant and Data Analytics Consultant. However, this is a 'go to market' approach, we'll only be hiring one of the roles. If you're interested in both, don't worry about applying twice, you can select 'yes' consider me for both roles in the application form (which only takes a few minutes to complete ).

About Nomensa

Nomensa is a strategic experience design agency based in Bristol, London, and Amsterdam. Combining our experience in psychology, interaction design and technology, we create world class digital experiences.  

We work closely with our extensive range of clients across multiple and diverse sectors to design award winning inclusive digital experiences that help millions of people. From blue chips to start-ups, from Deliveroo to the NHS, whatever the sector, we deliver experience excellence.  

We are part of the Sideshow Group, a global community of digital and marketing agencies with an ambitious growth agenda. We get to collaborate, share knowledge and learn. This means potential for all sorts of future development opportunities and variety of work across the Group.   

We hybrid work so you get to come together in the office to collaborate and connect, as well as have some working from home time if that's your preference. We also have other patterns support work/life balance (for example, part-time contracts, nomad working, sabbaticals etc).   

Requirements

What you’ll be doing

• Implement and maintain advanced tracking setups using GA4 and GTM to collect, analyse, and interpret data.
• Develop custom reports and dashboards in DataStudio to visualise data trends and provide actionable insights.
• Conduct in-depth analysis using BigQuery to uncover patterns, trends, and opportunities for optimisation.
• Collaborate with cross-functional teams to define key metrics, KPIs, and data collection strategies aligned with business objectives.
• Troubleshoot tracking issues, recommend solutions, and ensure data accuracy and consistency.
• Gaining an understanding of what our clients need by means of requirements elicitation, analytics discoveries, reporting and assessments
• Training on the tools and techniques we us along with key User Experience concepts
• Keep up-to-date with trends and best practice in analytics and the wider digital landscape
  

What we’re looking for

We are looking for an Analytics Consultant adept at thriving within a consultancy driven environment, where effective communication forms the bedrock of successful client engagement.  

Enthusiasm for pioneering analytics methodologies, coupled with a understanding of technology's role in driving actionable insights. The Analytics Consultant is entrusted with not only steering the design and delivery of analytics solutions but also championing innovative approaches to data analysis that resonate with clients' strategic objectives and business goals. 

The successful candidate must of course come from an analytics background, demonstrating fluency in harnessing data insights to drive strategic decisions and deliver bespoke client solutions. They will be passionate about designing and delivering innovative solutions.  

Ability to articulate complex analytics findings in a comprehensible manner is key. The ideal candidate will place a premium on clear, succinct communication, fostering alignment between clients and internal stakeholders to confidently gather requirements.  

Someone who can build robust data pipelines and configure Google tools such as GTM, GA4, BigQuery and Data Studio. 

Working with us will also provide opportunity to actively engage in thought leadership initiatives and events, serving as a representative of both themselves and Nomensa. 

Benefits

Our benefits for UK employees include:  

• Time to recharge: 25 days holiday (rising to 30 with length of service), your birthday off, holiday exchange scheme (up to 5 days), Summer Fridays.  
• Health & Wellbeing: BUPA cashback scheme, BUPA medical insurance (with service), 24/7 Employee Assistance Programme cycle to work scheme, Mental Health 1st Aiders 
• Financial wellbeing: competitive salaries, company pension contribution, , life insurance, financial wellbeing service, employee discount scheme 
• Life beyond work: time off for charity work, involvement in our employee groups on ED&I, mental health, environment, and regular socials and fun events arranged by our Chief Happiness Officers! 
  

The Application Process:   

Applicants will be shortlisted and selected for a short call with our Talent Acquisition Specialist. You'll talk experience, ambitions and salary expectations. From there the interview process will consist of two stages.  

If you’re excited about working for us but don’t have all the relevant skills, we would still love to hear from you. You may have relevant experience or transferable skills. We are all the better because of the many diverse backgrounds our employees have so don't let not ticking every box hold you back, give it a go! 

Interview Adjustments  

We do everything possible to create the best experience for candidates. We appreciate interviews can be challenging, so, please let us know if any adjustments will make you more comfortable or confident. For example, provide extra time or flexibility on assessments, have a chaperone on the interview, supply questions up front, or anything else that might help your application.   

Our Talent Acquisition Specialist Corrie ( ) can help with any questions during the recruitment process.   

Nomensa is an equal opportunities employer and positively encourages applications from suitably qualified and eligible candidates regardless of sex, race, disability, age, sexual orientation, gender reassignment, religion or belief, marital status, or pregnancy and maternity. 

• Assist in the collection, cleaning, and preprocessing of large datasets from various sources.
• Perform exploratory data analysis (EDA) to identify patterns, trends, and insights.
• Develop and implement machine learning models for predictive analytics and forecasting.
• Create compelling data visualisations and dashboards to communicate findings to stakeholders.
• Support the development and testing of new analytical tools and methodologies.
• Collaborate with cross-functional teams to understand business needs and translate them into data-driven solutions.
• Document methodologies, processes, and results clearly and concisely.
• Participate in team meetings, contributing ideas and insights.
• Learn and apply new data science techniques and technologies.
• Assist in A/B testing design and analysis.
• Contribute to the ongoing improvement of data quality and governance.

• Recent graduate with a Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
• Strong foundational knowledge of statistical concepts and machine learning algorithms.
• Proficiency in programming languages such as Python or R.
• Experience with data manipulation and analysis libraries (e.g., Pandas, NumPy, Scikit-learn).
• Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Tableau, Power BI).
• Excellent analytical and problem-solving skills.
• Strong written and verbal communication skills, with the ability to explain technical concepts to a non-technical audience.
• Self-motivated, curious, and eager to learn in a remote setting.
• Ability to work independently and manage time effectively.
• Understanding of SQL and database principles is desirable.
• Previous internship or project experience in data analysis is a plus.

• Develop and implement sophisticated statistical models and machine learning algorithms to solve business challenges.
• Clean, transform, and prepare large, complex datasets for analysis.
• Conduct exploratory data analysis (EDA) to identify patterns, trends, and anomalies.
• Build, train, and evaluate predictive models for forecasting, classification, and segmentation.
• Communicate complex findings and recommendations clearly and concisely to both technical and non-technical stakeholders through reports and presentations.
• Collaborate with engineering teams to deploy models into production environments.
• Stay current with the latest advancements in data science, machine learning, and artificial intelligence.
• Design and conduct experiments to test hypotheses and validate model performance.
• Contribute to the development of data-driven products and services.

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
• Proven experience in data science, machine learning, and statistical modelling.
• Proficiency in programming languages such as Python or R, and relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch, Pandas, NumPy).
• Experience with SQL and database management.
• Familiarity with big data technologies (e.g., Spark, Hadoop) is a plus.
• Strong understanding of statistical concepts and experimental design.
• Excellent analytical, problem-solving, and critical-thinking skills.
• Effective communication and presentation abilities.
• Experience with data visualisation tools (e.g., Tableau, Power BI) is desirable.

• Design, develop, and implement advanced statistical and machine learning models.
• Analyze large, complex datasets to identify trends, patterns, and insights related to customer behaviour.
• Develop customer segmentation, churn prediction, and lifetime value models.
• Build and deploy recommendation systems and personalization algorithms.
• Collaborate with business stakeholders to define analytical requirements and translate them into technical solutions.
• Create compelling data visualizations and reports to communicate findings effectively.
• Work with engineering teams to integrate models into production systems.
• Evaluate and recommend new data science tools and methodologies.
• Mentor junior data scientists and contribute to team knowledge sharing.
• Ensure data quality and integrity throughout the analytical process.

• Master's or PhD in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field.
• 5+ years of professional experience in data science or a related analytical role.
• Strong expertise in statistical modeling, machine learning algorithms (e.g., regression, classification, clustering, deep learning), and data mining.
• Proficiency in Python or R, including relevant data science libraries (e.g., scikit-learn, pandas, TensorFlow, PyTorch).
• Advanced SQL skills for data extraction and manipulation.
• Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
• Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps principles.
• Excellent communication and presentation skills, with the ability to explain complex technical concepts to non-technical audiences.
• Strong problem-solving and analytical thinking abilities.
• Experience in retail analytics or e-commerce is highly desirable.

• Develop and implement advanced statistical models and machine learning algorithms for insurance analytics.
• Conduct exploratory data analysis (EDA) to identify trends, patterns, and insights from large datasets.
• Build predictive models for pricing, risk assessment, fraud detection, and customer lifetime value.
• Design and execute A/B tests and other experiments to validate model performance.
• Clean, transform, and prepare data for modelling purposes.
• Work with stakeholders to define business problems and translate them into analytical solutions.
• Communicate complex findings and recommendations clearly to both technical and non-technical audiences.
• Develop and maintain data pipelines and ensure data quality.
• Stay abreast of the latest advancements in data science, machine learning, and insurance analytics.
• Mentor junior data scientists and contribute to the team's technical growth.

• Master's or Ph.D. in Data Science, Statistics, Computer Science, Mathematics, Economics, or a related quantitative field.
• Minimum of 5 years of professional experience as a Data Scientist, with a significant focus on the insurance or financial services industry.
• Proven expertise in statistical modelling, machine learning techniques (e.g., regression, classification, clustering, time series analysis), and deep learning.
• Proficiency in programming languages such as Python (with libraries like Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) or R.
• Experience with SQL for data extraction and manipulation.
• Familiarity with data visualization tools (e.g., Tableau, Power BI) is a plus.
• Strong understanding of insurance products, regulations, and business processes.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation abilities.
• Ability to work effectively both independently and as part of a collaborative team.

• Develop, validate, and deploy advanced statistical and machine learning models to address key business challenges in insurance.
• Analyze large, complex datasets to identify trends, patterns, and actionable insights related to underwriting, claims, fraud detection, and customer behavior.
• Design and implement A/B testing frameworks and experiments to evaluate the effectiveness of new strategies.
• Collaborate closely with business stakeholders, actuaries, and underwriters to translate business requirements into analytical solutions.
• Communicate complex analytical findings clearly and concisely to both technical and non-technical audiences through reports, presentations, and visualizations.
• Stay current with the latest advancements in data science, machine learning, and AI, and evaluate their applicability to the insurance domain.
• Mentor junior data scientists and contribute to the growth of the data science practice within the organization.
• Develop and maintain robust data pipelines and ensure data quality and integrity for analytical purposes.
• Contribute to the development of data governance and best practices.
• Present findings and recommendations to senior management, influencing strategic direction.

• Ph.D. or Master's degree in a quantitative field such as Data Science, Statistics, Computer Science, Mathematics, or a related discipline.
• 5+ years of experience in data science, with a proven track record of delivering impactful solutions, preferably within the insurance sector.
• Proficiency in programming languages such as Python or R, and expertise in relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Strong experience with SQL and working with large-scale databases.
• Deep understanding of various machine learning algorithms (e.g., regression, classification, clustering, time series analysis, deep learning).
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop).
• Excellent problem-solving skills, analytical thinking, and a passion for uncovering data-driven insights.
• Exceptional communication and presentation skills, with the ability to explain technical concepts to a non-technical audience.
• Experience with data visualization tools (e.g., Tableau, Power BI).
• Knowledge of insurance products, risk management, and actuarial principles is a significant advantage.

• Design, develop, and implement advanced statistical models and machine learning algorithms to solve complex business problems in insurance.
• Analyze large, complex datasets to identify trends, patterns, and actionable insights related to customer behavior, risk assessment, and operational efficiency.
• Develop predictive models for customer segmentation, churn prediction, risk scoring, and claims forecasting.
• Implement and deploy machine learning models into production environments, collaborating closely with data engineers and software developers.
• Conduct A/B testing and other experimental designs to evaluate the effectiveness of new models and strategies.
• Communicate complex analytical findings and recommendations clearly and concisely to both technical and non-technical stakeholders through reports and presentations.
• Stay up-to-date with the latest advancements in data science, machine learning, and artificial intelligence.
• Mentor junior data scientists and contribute to building a strong data-driven culture.
• Ensure data quality, integrity, and ethical considerations are maintained throughout all analytical processes.

• Master's degree or PhD in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field.
• Minimum of 5 years of professional experience in data science, with a strong focus on insurance or financial services.
• Proficiency in programming languages such as Python or R, and experience with relevant data science libraries (e.g., scikit-learn, TensorFlow, PyTorch, pandas).
• Expertise in statistical modeling, machine learning techniques (e.g., regression, classification, clustering, time series analysis), and data mining.
• Experience with SQL and working with large relational databases.
• Familiarity with cloud platforms (AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop) is a plus.
• Excellent problem-solving, analytical, and critical thinking skills.
• Strong communication and presentation skills, with the ability to explain complex concepts to diverse audiences.
• Proven ability to work independently and collaboratively in a fully remote setting.

• Assist in the collection, cleaning, and pre-processing of large datasets.
• Develop, test, and deploy machine learning models for predictive analytics.
• Conduct exploratory data analysis to uncover trends and insights.
• Collaborate with senior data scientists and engineers on project development.
• Create data visualisations and reports to communicate findings to stakeholders.
• Participate in code reviews and contribute to the team's knowledge base.
• Stay abreast of the latest advancements in data science and machine learning.
• Contribute to the continuous improvement of data science workflows and methodologies.
• Assist in the documentation of models and analytical processes.
• Engage in team meetings and contribute innovative ideas.

• Recent graduate with a degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
• Solid understanding of statistical concepts and machine learning algorithms (e.g., regression, classification, clustering).
• Proficiency in programming languages such as Python or R.
• Familiarity with data manipulation libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch).
• Excellent analytical and problem-solving skills.
• Strong communication and collaboration abilities, essential for a remote role.
• Eagerness to learn and adapt to new technologies and methodologies.
• Ability to work independently and manage time effectively in a remote setting.
• Prior internship or project experience in data science is a plus.