Data Scientist - Machine Learning Engineer

  • Location

    Cambridgeshire, England

  • Sector:

    Data

  • Job type:

    Contract

  • Salary:

    £550 - £650 per day

  • Contact:

    James Jarvis

  • Contact email:

    james.jarvis@xcede.co.uk

  • Job ref:

    HQ00028340_1564678390

  • Duration:

    6-12 Months

  • Startdate:

    ASAP

DATA SCIENCE / MACHINE LEARNING / PYTHON / AWS / API / FLASK

Data Scientist (Machine Learning)
Rate:
Start: ASAP
Location: Cambridge
Duration: 6 Months

My client is currently recruiting for a Data Scientist focusing on Machine Learning as they need to productionise their machine learning models to classify tax submissions.

The ideal candidate will possess a blend of mathematical expertise, computational science skills, strong communication skills and software engineering experience. They will have experience of applying data science at scale.

This individual will deliver tangible value through building pipelines, productionising data science solutions, running machine learning tests, re-training models when required and maintaining solutions. The right individual is also expected to build solutions using statistical analysis, machine learning or other methodologies.

*Someone in essence who will take pre-written algorithms (written in python), make them airtight and containerise and move to cloud
*Need to understand how M/Learning algorithms are written and underlying theory but does not need to be an expert in writing themselves
*Need to understand how to split an ML algorithm into different cloud microservices and have experience implementing pipelines of such microservices.
*Need to be a strong software developer and able to design & implement scalable cloud solutions.
*Able to guide IT solution architects in authoritative way on the topic of ML deployment and help craft solution blueprints.

Required:

*Data Science - math, statistics, programming, M/Learning, software development - unit testing CIDI deployment = all expected skills
*API's - building services
*Microservices - splitting ML algorithms/flows into multiple services
*Scaling services in cloud environment and integrating into cloud products.


Key Accountabilities

*Establishing strong working relationships with the Data Science team and customers to develop an in-depth understanding of business priorities and early insight into changing needs
*Translating unstructured, complex business problems into appropriate solution
*Developing a robust understanding of relevant data sources and their provenance, quality and structure
*Working with engineering and architecture to support large scale data preparation, the optimization of analytics platforms and the industrialization of proven analytics methods
*Working with data science suppliers as needed to augment the clients' capability
*Undertaking business analysis and business process analysis as part of solution design process
*Being an active member of the Analytics team, You will benefit from, their expanding bank of Data Science algorithms and work efficiently with the data science workstations. You will be involved in testing and assessing the quality of new tools.

Candidate Knowledge, Skills and Experience

*3 years+ commercial experience with practical examples of success in implementing production ready data science solutions
*PhD or MSc in Computer Science, Statistics, Operations Research, Mathematics or related quantitative field
*Experience of software engineering and a strong understanding of software design patterns, particularly microservices, with strong understanding of AWS environment and products
*Experience of working in SOX and GXP compliant environments is desirable
*Experience using Git and deploying code in different staging environments
*Strong background in machine learning using unsupervised and supervised methods. Knowledge of deep learning would be an advantage
*Experience building APIs using frameworks like Python/FLASK, loosely-coupled web applications on recognized frameworks and experience using docker containerization
*Use of Data Science modelling and scripting tools eg. R, Python and Data Science Notebooks (e.g. Jupyter)
*Data Manipulation/preparation skills: SQL, Python - Pandas, R, Hadoop Sufficient knowledge of designing feedback pipelines for re-training machine learning models
*Ability to independently interpret different mathematical measures of accuracy and suggest ways to improve data science models