Põhiline

Lead Data Scientist

slough, south east england, United Kingdom Ettevõte: JR UK Klient / Tööandja: Harnham
Postitatud: 20.05.2026
Sulgemiskuupäev: 04.07.2026
Tööviide: a161a7aca338f13cccc57c2d71a294d5

Tööinfo

Asukoht
slough, south east england, United Kingdom
Ettevõte
JR UK
Klient / Tööandja
Harnham
Tööviide
a161a7aca338f13cccc57c2d71a294d5
Kuulutuse tüüp
Põhiline
EL-i tööluba nõutav
Ei
Postitatud
20.05.2026
Sulgemiskuupäev
04.07.2026

Tööülesannete kirjeldus

Lead Data Scientist

Up to £125,000

London (Hybrid, 2-3 days onsite per week)



Company:

This private equity and investment start-up are focused on streamlining and improving performance by reimagining value creation capabilities. They are utilising cutting-edge AI to generate performance insights on each aspect of the investment lifecycle.



Responsibilities:

  • Build machine learning predictive models to anticipate/avoid asset downtime and reducing customer churn
  • Evaluate and optimise all models, identifying areas for development
  • Work end-to-end both building and deploying models
  • Stay updated with the latest developments in ML/AI, and related fields to keep the company at the forefront of technological advancements
  • Stay updated on emerging technologies, trends, to recommend and implement innovative solutions that drive business value.
  • Remaining technically very hands on whilst mentoring



Requirements:

  • MSc or PhD Degree in Computer Science, Artificial Intelligence, Mathematics, Statistics or related fields.
  • Strong coding skills in Python and SQL
  • Strong communication skills, with the ability to work effectively in a fast-paced, collaborative environment.

Oskused

apply blended learning apply for research funding apply research ethics and scientific integrity principles in research activities build recommender systems Business Analytics Business Intelligence collect ICT data communicate with a non-scientific audience Computational Biology Computer Simulation conduct research across disciplines create data models Data Engineering data ethics Data Mining Data Models data quality assessment Data Science data visualisation software define data quality criteria deliver visual presentation of data demonstrate disciplinary expertise design database in the cloud design database scheme develop data processing applications develop professional network with researchers and scientists Digital Curation disseminate results to the scientific community draft scientific or academic papers and technical documentation empirical analysis establish data processes evaluate research activities execute analytical mathematical calculations Hadoop handle data samples Healthcare Analytics image recognition implement data quality processes increase the impact of science on policy and society information categorisation Information Extraction integrate gender dimension in research integrate ICT data interact professionally in research and professional environments interpret current data LDAP LINQ make data-driven decisions manage data manage data collection systems manage findable accessible interoperable and reusable data manage ICT data architecture manage ICT data classification manage intellectual property rights manage open publications manage personal professional development manage research data Marketing Analytics mathematical modelling MDX mentor individuals multidisciplinary research N1QL normalise data online analytical processing operate open source software perform data cleansing perform data mining perform project management perform scientific research promote open innovation in research promote the participation of citizens in scientific and research activities promote the transfer of knowledge publish academic research quantitative analysis query languages report analysis results Research Design resource description framework query language Scientific Computing scientific literature Social Network Analysis SPARQL speak different languages State Estimation statistical modeling techniques Statistics synthesise information teach in academic or vocational contexts think abstractly Unstructured Data use data processing techniques use databases use spreadsheets software visual presentation techniques write scientific publications XQuery

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