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FPMS Coding and Development Lead in Newport

2 months ago

Newport, United Kingdom Concept Resourcing Full time

FPMS Coding and Development Lead

£25.36 per hour
INSIDE IR35
6 Month Contract with expected extension
Full Time - 37 hours per week
Hybrid - 2 to 3 days per week in either Newport, South Wales, Titchfield, Hampshire, Edinburgh or Darlington offices.

My client are a large prestigious government organisation and are looking for Research Officers to come and join their team on an initial 6 month basis, paying £25.36 per hour, Inside IR35.

LIVE SC CLEARANCE IS HIGHLY DESIRABLE FOR THIS ROLE

Job Description

The purpose of the Junior Data Scientist (HEO), Data Scientist (SEO) and Senior Data Scientist (G7) is to code, manipulate, engineer, model and analyse data and/or data processes to underpin Data Architecture and support internal and external users.

Key Responsibilities

Key Responsibilities: A data scientist identifies complex business problems whilst leveraging data value. They work as part of a multidisciplinary team with data architects, data engineers, analysts and others.

Data scientists:

  • work with policy and operations teams to understand where data science can add value
  • support strategic and operational decision making in order to create impact
  • source, access, manipulate and engineer data processes with data that typically have characteristics of volume, velocity and variety, or both
  • build credible statistical models from the data and use best coding practices to generate reproducible work
  • may draw on other technical and analytical standards from across government and industry
  • adhere to the data science ethics framework
  • are open minded and demonstrate strong intellectual curiosity
  • have an interdisciplinary focus, using techniques and knowledge from across the scientific spectrum
  • explore and visualise the data to present the 'story' of the data in a meaningful way, and to a range of technical and non-technical audiences
  • use an evolving range of data analysis tools and techniques, including open source, some of which must be learnt quickly, as and when required
  • continuously seek to expand a range of technical skills in addition to their leadership and communications development
  • propagate data science skills in other teams

Data scientists:

  • have recognised technical ability in a number of data science specialisms and provide detailed technical advice on their area of expertise
  • promote and present data science work both within and outside of the organization
  • engage with stakeholders and champion the value of data science work
  • line manage and mentor junior data scientists
  • manage small project teams

Skills for SEO Practioner

Applied maths, statistics and scientific practices: Understands how algorithms are designed, optimised and applied at scale. Can select and use appropriate statistical methods for sampling, distribution assessment, bias and error. Understands problem structuring methods and can evaluate when each method is appropriate. Applies scientific methods through experimental design, exploratory data analysis and hypothesis testing to reach robust conclusions.

Data engineering and manipulation: Works with other technologists and analysts to integrate and separate data feeds in order to map, produce, transform and test new scalable data products that meet user needs. Has a demonstrable understanding of how to expose data from systems (for example, through APIs), link data from multiple systems and deliver streaming services. Works with other technologists and analysts to understand and make use of different types of data models. Understands and can make use of different data engineering tools for repeatable data processing and is able to compare between different data models. Understands how to build scalable machine learning pipelines and combine feature engineering with optimisation methods to improve the data product performance.

Data science innovation: Recognises and exploits business opportunities to ensure more efficient and effective ways to use data science. Explores ways of utilising new data science tools and techniques to tackle business and organisational challenges. Demonstrates strong intellectual curiosity with an interdisciplinary approach, drawing on innovation in academia and industry.

Developing data science capability: Continuously develops data science knowledge, utilising multiple sources. Shares data science practices across departments and in industry, promoting professional development and use of best practice across all capabilities identified for data scientists. Focuses on recruitment and induction of data scientists.

Domain expertise: Understands the context of the business, its processes, data and priorities. Applies data science techniques to present, communicate and disseminate data science products effectively, appropriately and with high impact. Uses the most appropriate medium to visualise data to tell compelling and actionable stories relevant for business goals. Maintains a user focus to design solutions that meet user needs, taking account of agreed cross-government ethics standards.

Programming and build (data science): Uses a range of coding practices to build scalable data products that can be used by strategic or operational users and can be further integrated into business systems. Works with technologists to design, create, test and document these data products. Works in accordance with agreed software development standards, including security, accessibility and version control.

Understanding analysis across the life cycle (data science): Understands the different phases of product delivery and is able to plan and run the analysis for these. Able to contribute to decision-making throughout the life cycle. Works in collaboration with user researchers, Developers and other roles throughout the life cycle. Understands the value of analysis, how to contribute with impact and which data sources, analytical techniques and tools can be used at each point throughout the life cycle.