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.
17/01/2025
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.
17/01/2025
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.
Job Title: Microsoft Dynamics 365 Field Service Architect Location: UK (Remote with monthly 1-2 visits to Scrumconnect office) Employment Type: Fixed-Term Contract (12 months) Immediate Joiners Preferred Pay: Industry standards About the Role We are seeking an experienced Microsoft Dynamics 365 Field Service Architect to join our team. In this role, you will play a key part in designing and implementing solutions that enhance field service operations, ensuring seamless scheduling, resource optimization, and exceptional customer service. Your expertise will enable our clients to meet customer demands efficiently while leveraging cutting-edge technologies. Key Responsibilities Solution Implementation : Design and implement solutions to schedule resources effectively to meet customer requirements. Optimize resource utilization by aligning skills, locations, and availability. Equip frontline workers with tools and information for delivering exceptional service. Build and maintain service histories for customer assets. Configuration and Customization : Configure default administration areas of the Field Service application. Set up and optimize resources, scheduling (Universal Resource Scheduling), and service agreements. Manage work orders, incident types, and bookings for seamless operations. Configure customer assets and integrate them into the service life cycle. Install and configure the Field Service mobile app for on-the-go support. Enhancements and Continuous Improvement : Enhance Field Service applications based on evolving customer requirements. Collaborate with stakeholders to identify opportunities for innovation and optimization. Essential Skills and Qualifications Experience : A minimum of 3 years of hands-on experience in Microsoft Dynamics 365 Field Service implementations. Technical Expertise : Proficiency in D365 Field Services and Power Platform . Strong understanding of resource scheduling, work order management, and mobile app configurations. Certification : Preferably MB-240: Microsoft Dynamics 365 Field Service Functional Consultant Associate certification. Soft Skills : Excellent problem-solving and analytical skills. Strong communication and stakeholder management abilities. Why Join Us? Opportunity to work with cutting-edge technologies in a dynamic environment. Flexible remote working with occasional visits to our office. Be part of a forward-thinking team dedicated to delivering exceptional solutions.
16/01/2025
Job Title: Microsoft Dynamics 365 Field Service Architect Location: UK (Remote with monthly 1-2 visits to Scrumconnect office) Employment Type: Fixed-Term Contract (12 months) Immediate Joiners Preferred Pay: Industry standards About the Role We are seeking an experienced Microsoft Dynamics 365 Field Service Architect to join our team. In this role, you will play a key part in designing and implementing solutions that enhance field service operations, ensuring seamless scheduling, resource optimization, and exceptional customer service. Your expertise will enable our clients to meet customer demands efficiently while leveraging cutting-edge technologies. Key Responsibilities Solution Implementation : Design and implement solutions to schedule resources effectively to meet customer requirements. Optimize resource utilization by aligning skills, locations, and availability. Equip frontline workers with tools and information for delivering exceptional service. Build and maintain service histories for customer assets. Configuration and Customization : Configure default administration areas of the Field Service application. Set up and optimize resources, scheduling (Universal Resource Scheduling), and service agreements. Manage work orders, incident types, and bookings for seamless operations. Configure customer assets and integrate them into the service life cycle. Install and configure the Field Service mobile app for on-the-go support. Enhancements and Continuous Improvement : Enhance Field Service applications based on evolving customer requirements. Collaborate with stakeholders to identify opportunities for innovation and optimization. Essential Skills and Qualifications Experience : A minimum of 3 years of hands-on experience in Microsoft Dynamics 365 Field Service implementations. Technical Expertise : Proficiency in D365 Field Services and Power Platform . Strong understanding of resource scheduling, work order management, and mobile app configurations. Certification : Preferably MB-240: Microsoft Dynamics 365 Field Service Functional Consultant Associate certification. Soft Skills : Excellent problem-solving and analytical skills. Strong communication and stakeholder management abilities. Why Join Us? Opportunity to work with cutting-edge technologies in a dynamic environment. Flexible remote working with occasional visits to our office. Be part of a forward-thinking team dedicated to delivering exceptional solutions.
