Typical Responsibilities: Develop and maintain data products. Data Engineering teams are responsible for the delivery and operational stability of the data products built and provide ongoing support for those products. Data Engineers work within, and contribute to, the overall data development life cycle process as part of multi-functional Agile delivery teams focused on one or more products. Data Engineers should have the following essential skills: ? Ability to shape solutions in a fit for purpose way, following the agreed principles and contribute to the overall Data Engineer development life cycle. ? Ability to support the day-to-day testing and produce robust test plans to ensure quality solutions and the live running of data products and services. ? Ability to perform data profiling and quality measurements, ensure data quality/accuracy, knowledge of structured and unstructured data concepts, developing automated data ingest routines, workflows/mappings and data exploitation patterns and data analysis. ? Own the stability of products designed, including the on-going robustness, resilience, and stability of these products. ? Ability to identify, manage, and resolve issues/problems preventing the delivery or continuous development of products, using analytical skills to develop query solutions from specification to conclusion and implementation. ? Ability to support the growth of the team, by keeping abreast of market and industry trends, and sharing knowledge and experience with the rest of the team. Take responsibility for their own learning and development continuously improving knowledge and skills. Typical Data Engineering Experience required (8yrs+): ? Knowledge and experience of Azure/AWS Cloud data solution provision. (Preference will be given to those who hold relevant certifications) ? Proficient in SQL. ? Ability to develop and deliver complex visualisation, reporting and dashboard solutions using tools like Power BI. ? Enterprise-scale experience with ETL tools (Informatica or similar cloud native solutions). ? Experience of data modelling and transforming raw data into datasets and extracts. ? Experience of working in a large project/scale complex organisation and knowledge of migrating Legacy capabilities. ? Experience in Agile. ? Ability to analyse and collect information and evidence, identify problems and opportunities, and ensure recommendations fit with strategic business objectives. ? Experience of building team capability through role modelling, mentoring, and coaching. ? Ability to manage relationships with non-technical colleagues and can work in a collaborative, inclusive way.
20/12/2024
Full time
Typical Responsibilities: Develop and maintain data products. Data Engineering teams are responsible for the delivery and operational stability of the data products built and provide ongoing support for those products. Data Engineers work within, and contribute to, the overall data development life cycle process as part of multi-functional Agile delivery teams focused on one or more products. Data Engineers should have the following essential skills: ? Ability to shape solutions in a fit for purpose way, following the agreed principles and contribute to the overall Data Engineer development life cycle. ? Ability to support the day-to-day testing and produce robust test plans to ensure quality solutions and the live running of data products and services. ? Ability to perform data profiling and quality measurements, ensure data quality/accuracy, knowledge of structured and unstructured data concepts, developing automated data ingest routines, workflows/mappings and data exploitation patterns and data analysis. ? Own the stability of products designed, including the on-going robustness, resilience, and stability of these products. ? Ability to identify, manage, and resolve issues/problems preventing the delivery or continuous development of products, using analytical skills to develop query solutions from specification to conclusion and implementation. ? Ability to support the growth of the team, by keeping abreast of market and industry trends, and sharing knowledge and experience with the rest of the team. Take responsibility for their own learning and development continuously improving knowledge and skills. Typical Data Engineering Experience required (8yrs+): ? Knowledge and experience of Azure/AWS Cloud data solution provision. (Preference will be given to those who hold relevant certifications) ? Proficient in SQL. ? Ability to develop and deliver complex visualisation, reporting and dashboard solutions using tools like Power BI. ? Enterprise-scale experience with ETL tools (Informatica or similar cloud native solutions). ? Experience of data modelling and transforming raw data into datasets and extracts. ? Experience of working in a large project/scale complex organisation and knowledge of migrating Legacy capabilities. ? Experience in Agile. ? Ability to analyse and collect information and evidence, identify problems and opportunities, and ensure recommendations fit with strategic business objectives. ? Experience of building team capability through role modelling, mentoring, and coaching. ? Ability to manage relationships with non-technical colleagues and can work in a collaborative, inclusive way.
Job Summary: We are looking for a talented Data Scientist who can characterise business problems, develop data-driven solutions, and communicate insights effectively to stakeholders. The successful candidate will have a strong foundation in statistics, programming skills, and experience with big data platforms. This role requires excellent problem-solving skills, leadership abilities, and the ability to work collaboratively with teams. Requirements: Education: Bachelor's/Master's degree in Machine Learning or Computer Science, Statistics, or related field. Preference will be given to those candidates with strong educational background as well as relevant certifications in the mentioned fields. Skills: Strong foundation in statistics and programming (R/Python). Experience with data preparation, visualisation, and model building. Knowledge of big data platforms (Hadoop, Spark) and SQL/NoSQL databases. Experience: 3+ years of experience as a Data Scientist or related role. Typical Responsibilities: Develop and maintain data products. Data Engineering teams are responsible for the delivery and operational stability of the data products built and provide ongoing support for those products. Data Engineers work within, and contribute to, the overall data development life cycle process as part of multi-functional Agile delivery teams focused on one or more products. Data Scientists should have the following skills: Data science foundation - a data scientist must be able to: - Characterise a business problem - Formulate a hypothesis - Demonstrate the use of methodologies in the analytics cycle - Plan for the execution Understanding the data science workflow and recognizing the importance of each element of the process is critical for successful implementations. Statistics and programming foundation (Analysis & Visualisation) - the competencies in this area are focused on the knowledge of key statistics concepts and methods essential to finding structure in data and making predictions. Programming skills (R/Python) or other statistical programming skills are essential -and the ability to visualise data, extract insights and communicate the insights in a clear and concise manner. Data preparation - to ensure usable data sets, the key competencies required are: - Identifying and collecting the data required - Manipulating, transforming and cleaning the data A data scientist must deal with data anomalies such as missing values, outliers, unbalanced data and data normalisation. Model building - this stage is the core of the data science execution, where different algorithms are used to train the data and the best algorithm is selected. A data scientist should know: - Multiple modelling techniques - Model validation and selection techniques A data scientist must understand the use of different methodologies to get insight from the data and translate the insight into business value. Model deployment - an ML model is valuable when it's integrated into an existing production environment and used to make business decisions. Deploying a validated model and monitoring it to maintain the accuracy of the results is a key skill. Big data foundation - a data scientist deals with a large volume of structured and unstructured data, they must demonstrate understanding of how big data is used, the big data ecosystem and its major components. The data scientist must also demonstrate expertise with big data platforms, such as Hadoop and Spark and master SQL and NoSQL. Leadership and professional development - data scientists must be good problem solvers. They must understand the opportunity before implementing the solution, work in a rigorous and complete manner, and explain their findings. A data scientist needs to understand the concepts of analysing business risk, making improvements in processes and how systems engineering works.
20/12/2024
Full time
Job Summary: We are looking for a talented Data Scientist who can characterise business problems, develop data-driven solutions, and communicate insights effectively to stakeholders. The successful candidate will have a strong foundation in statistics, programming skills, and experience with big data platforms. This role requires excellent problem-solving skills, leadership abilities, and the ability to work collaboratively with teams. Requirements: Education: Bachelor's/Master's degree in Machine Learning or Computer Science, Statistics, or related field. Preference will be given to those candidates with strong educational background as well as relevant certifications in the mentioned fields. Skills: Strong foundation in statistics and programming (R/Python). Experience with data preparation, visualisation, and model building. Knowledge of big data platforms (Hadoop, Spark) and SQL/NoSQL databases. Experience: 3+ years of experience as a Data Scientist or related role. Typical Responsibilities: Develop and maintain data products. Data Engineering teams are responsible for the delivery and operational stability of the data products built and provide ongoing support for those products. Data Engineers work within, and contribute to, the overall data development life cycle process as part of multi-functional Agile delivery teams focused on one or more products. Data Scientists should have the following skills: Data science foundation - a data scientist must be able to: - Characterise a business problem - Formulate a hypothesis - Demonstrate the use of methodologies in the analytics cycle - Plan for the execution Understanding the data science workflow and recognizing the importance of each element of the process is critical for successful implementations. Statistics and programming foundation (Analysis & Visualisation) - the competencies in this area are focused on the knowledge of key statistics concepts and methods essential to finding structure in data and making predictions. Programming skills (R/Python) or other statistical programming skills are essential -and the ability to visualise data, extract insights and communicate the insights in a clear and concise manner. Data preparation - to ensure usable data sets, the key competencies required are: - Identifying and collecting the data required - Manipulating, transforming and cleaning the data A data scientist must deal with data anomalies such as missing values, outliers, unbalanced data and data normalisation. Model building - this stage is the core of the data science execution, where different algorithms are used to train the data and the best algorithm is selected. A data scientist should know: - Multiple modelling techniques - Model validation and selection techniques A data scientist must understand the use of different methodologies to get insight from the data and translate the insight into business value. Model deployment - an ML model is valuable when it's integrated into an existing production environment and used to make business decisions. Deploying a validated model and monitoring it to maintain the accuracy of the results is a key skill. Big data foundation - a data scientist deals with a large volume of structured and unstructured data, they must demonstrate understanding of how big data is used, the big data ecosystem and its major components. The data scientist must also demonstrate expertise with big data platforms, such as Hadoop and Spark and master SQL and NoSQL. Leadership and professional development - data scientists must be good problem solvers. They must understand the opportunity before implementing the solution, work in a rigorous and complete manner, and explain their findings. A data scientist needs to understand the concepts of analysing business risk, making improvements in processes and how systems engineering works.
Senior Level - SFIA5 Salary: £50k-65k dependent on experience Location: Coventry/Hybrid About Scrumconnect: Scrumconnect is a leading force in technology consultancy, proudly contributing to over 20% of the UK's most significant citizen-facing public services. Our award-winning team has made a substantial impact, delivering more than 64 services in the past two years alone. This work has not only reached over 50 million citizens but also achieved considerable savings for the taxpayer, amounting to over £25 million. At Scrumconnect, we foster a community of talented consultants who thrive on collaboration, sharing knowledge, and continuous learning to address and solve complex challenges. Our mission is to combine advanced software engineering, human-focused design, and data-driven insights to deliver unparalleled service to our clients. A lead data scientist is a leader of data science, quite often with responsibility for managing and developing teams. At this role level, you will: have a broad knowledge of data science techniques, use cases and potential impact, as well as the tools and technologies have extensive experience in scoping, designing and delivering data science outputs and products work collaboratively with a range of experts in support of organisational objectives communicate effectively and challenge delivery plans and priorities appreciate and understand data ethics, data preparation and manipulation appreciate and understand delivery methods, and how to deliver supported solutions at scale Skills: Applied maths practises (Level: Expert) identify opportunities to develop statistical insight, reports and models to support organisational objectives, while collaborating across the organisation effectively critique statistical analyses use a variety of data analytics techniques (such as data mining and prescriptive and predictive analytics) for complex data analysis through the whole data life cycle use model outputs to produce evidence and help design services and policies understand a broad range of statistical tools, particularly those deployed within the organisation, and can use these appropriately and help others to use them Data Engineering (Level Expert) help to identify the data engineering requirements for any data science product, while working with data engineers and data scientists to design and deliver those products into the organisation effectively understand the need to cleanse and prepare data before including it in data science products and can put reusable processes and checks in place understand a broad range of architectures, including cloud and on-premise, and data manipulation and transformation tools deployed within the organisation, and can use these tools appropriately and help others use them Data Science Innovation (Expert) be a leader in the data science space demonstrate in-depth knowledge of data science tools and techniques, which you can use to solve problems creatively and to create opportunities for your team act as a coach, inspiring curiosity and creativity in others demonstrate in-depth knowledge of your chosen profession and keep up to date with changes in the industry challenge the status quo and always look for ways to improve data science One example of such work is proven experience to build Recurrent Neural Network (either in R or Python) from underlying data sets (after performing EDA) Delivering business impact (Level Practitioner) lead and support your organisation area by using data science to create change identify opportunities to develop data science products to support organisational objectives, while collaborating across the organisation to fulfil goals show an understanding of the role of user research, and can design and manage processes to gather and establish user needs communicate relevant and compelling stories effectively and present analysis and data visualisations clearly to get across complex messages work with colleagues to implement scalable data science products, and to understand maintenance requirements Ethics and Privacy (Level Expert) show an understanding of how ethical issues fit into a wider context and can work with relevant stakeholders stay up to date with developments in data ethics standards and legislation frameworks, using these to improve processes in your work area identify and respond to ethical concerns in your area of responsibility Programming for Data Science (Level Expert) write complex programs and scripts seek to make code open source where appropriate supervise Junior Analysts and set coding standards for your team understand software architecture and how to write efficient, optimised code perform user testing on products prior to launch Product Delivery (Level Expert) understand the differences between delivery methods, such as Agile and waterfall, and can set out how your team should use and adapt these methods lead a team through the different phases of the product delivery life cycle collaborate with the product manager to influence the direction of work identify and involve relevant teams to smoothly deliver data science products into the organisation, ensuring these products inform decision making ensure products are monitored, maintained and continually improved, engaging and working with others where necessary have oversight of any data science features implemented within products or services Knowledge of Public Sector Standards Government Digital Service (GDS): Familiarity with GDS service standards and the Technology Code of Practice. These skills reflect the need for both technical depth and the ability to navigate the unique demands of the UK public sector environment. Desired Qualifications Certifications in Azure, Databricks, or related technologies. Experience with public sector data initiatives and compliance requirements. Proven expertise and experience with machine learning and artificial intelligence concepts What our offer includes 28 days holiday inc. bank holidays 1 day Birthday leave after 1 year service 2 additional days after 2 years service Pension: 4% employee, 3% employer BUPA Health Cover AIG Life Cover Rewards Gateway On job training
17/12/2024
Full time
Senior Level - SFIA5 Salary: £50k-65k dependent on experience Location: Coventry/Hybrid About Scrumconnect: Scrumconnect is a leading force in technology consultancy, proudly contributing to over 20% of the UK's most significant citizen-facing public services. Our award-winning team has made a substantial impact, delivering more than 64 services in the past two years alone. This work has not only reached over 50 million citizens but also achieved considerable savings for the taxpayer, amounting to over £25 million. At Scrumconnect, we foster a community of talented consultants who thrive on collaboration, sharing knowledge, and continuous learning to address and solve complex challenges. Our mission is to combine advanced software engineering, human-focused design, and data-driven insights to deliver unparalleled service to our clients. A lead data scientist is a leader of data science, quite often with responsibility for managing and developing teams. At this role level, you will: have a broad knowledge of data science techniques, use cases and potential impact, as well as the tools and technologies have extensive experience in scoping, designing and delivering data science outputs and products work collaboratively with a range of experts in support of organisational objectives communicate effectively and challenge delivery plans and priorities appreciate and understand data ethics, data preparation and manipulation appreciate and understand delivery methods, and how to deliver supported solutions at scale Skills: Applied maths practises (Level: Expert) identify opportunities to develop statistical insight, reports and models to support organisational objectives, while collaborating across the organisation effectively critique statistical analyses use a variety of data analytics techniques (such as data mining and prescriptive and predictive analytics) for complex data analysis through the whole data life cycle use model outputs to produce evidence and help design services and policies understand a broad range of statistical tools, particularly those deployed within the organisation, and can use these appropriately and help others to use them Data Engineering (Level Expert) help to identify the data engineering requirements for any data science product, while working with data engineers and data scientists to design and deliver those products into the organisation effectively understand the need to cleanse and prepare data before including it in data science products and can put reusable processes and checks in place understand a broad range of architectures, including cloud and on-premise, and data manipulation and transformation tools deployed within the organisation, and can use these tools appropriately and help others use them Data Science Innovation (Expert) be a leader in the data science space demonstrate in-depth knowledge of data science tools and techniques, which you can use to solve problems creatively and to create opportunities for your team act as a coach, inspiring curiosity and creativity in others demonstrate in-depth knowledge of your chosen profession and keep up to date with changes in the industry challenge the status quo and always look for ways to improve data science One example of such work is proven experience to build Recurrent Neural Network (either in R or Python) from underlying data sets (after performing EDA) Delivering business impact (Level Practitioner) lead and support your organisation area by using data science to create change identify opportunities to develop data science products to support organisational objectives, while collaborating across the organisation to fulfil goals show an understanding of the role of user research, and can design and manage processes to gather and establish user needs communicate relevant and compelling stories effectively and present analysis and data visualisations clearly to get across complex messages work with colleagues to implement scalable data science products, and to understand maintenance requirements Ethics and Privacy (Level Expert) show an understanding of how ethical issues fit into a wider context and can work with relevant stakeholders stay up to date with developments in data ethics standards and legislation frameworks, using these to improve processes in your work area identify and respond to ethical concerns in your area of responsibility Programming for Data Science (Level Expert) write complex programs and scripts seek to make code open source where appropriate supervise Junior Analysts and set coding standards for your team understand software architecture and how to write efficient, optimised code perform user testing on products prior to launch Product Delivery (Level Expert) understand the differences between delivery methods, such as Agile and waterfall, and can set out how your team should use and adapt these methods lead a team through the different phases of the product delivery life cycle collaborate with the product manager to influence the direction of work identify and involve relevant teams to smoothly deliver data science products into the organisation, ensuring these products inform decision making ensure products are monitored, maintained and continually improved, engaging and working with others where necessary have oversight of any data science features implemented within products or services Knowledge of Public Sector Standards Government Digital Service (GDS): Familiarity with GDS service standards and the Technology Code of Practice. These skills reflect the need for both technical depth and the ability to navigate the unique demands of the UK public sector environment. Desired Qualifications Certifications in Azure, Databricks, or related technologies. Experience with public sector data initiatives and compliance requirements. Proven expertise and experience with machine learning and artificial intelligence concepts What our offer includes 28 days holiday inc. bank holidays 1 day Birthday leave after 1 year service 2 additional days after 2 years service Pension: 4% employee, 3% employer BUPA Health Cover AIG Life Cover Rewards Gateway On job training
