DATA SCIENTIST

To identify trends, patterns, and insights from raw data, data scientists use a variety of tools, data mining, statistical techniques, algorithms, and machine learning principles. Data scientists interpret data, uncover insights, and spot opportunities to help businesses make better decisions.

LOCATION

UK-London

EMPLOYMENY TYPE

Permanent

JOB DESCRIPTION - DATA SCIENTIST

  • Collaborate with stakeholders across the organisation to explore ways to use company data to create business solutions.

  • To promote optimization and improvement of product development, marketing approaches, and business strategies, data from company databases is mined and analysed.

  • Examine the efficacy and precision of new data sources and data collection procedures.

  • To apply to data sets, create bespoke data models and algorithms.

  • Increase and optimise customer experiences, revenue creation, ad targeting, and other company results via predictive modelling.

  • Develop an A/B testing framework for the organisation and evaluate model quality.

  • To implement models and track outcomes, collaborate with various functional teams.

  • Develop monitoring and analysis systems and technologies.

QUALIFICATION - DATA SCIENTIST

  • Excellent problem-solving abilities, with a focus on product creation.

  • Experience manipulating data and extracting insights from huge data sets using statistical computer languages (R, Python, SLQ, etc.).

  • Working with and designing data architectures is a plus.

  • Knowledge of a variety of machine learning approaches (clustering, decision tree learning, artificial neural networks, and so on) as well as their benefits and limitations in practise.

  • Advanced statistical techniques and ideas (regression, characteristics of distributions, statistical tests and suitable application, etc.) as well as application experience are required.

  • Knowledge of a variety of machine learning approaches (clustering, decision tree learning, artificial neural networks, and so on) as well as their benefits and limitations in practise.

  • Advanced statistical techniques and ideas (regression, characteristics of distributions, statistical tests and suitable application, etc.) as well as application experience are required.

  1. Coding skills and experience in a variety of languages, including C, C++, Java, JavaScript, and others.

  2. GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, and other statistical and data mining techniques are examples of knowledge and experience.

  3. Experience with databases and statistical programming languages such as R, Python, SLQ, and others.

  4. Redshift, S3, Spark, DigitalOcean, and other web services experience

  5. Regression, simulation, scenario analysis, modelling, clustering, decision trees, neural networks, and other sophisticated machine learning methods and statistics:

  6. Experience with data from third-party sources such as Google Analytics, Site Catalyst, Coremetrics, and AWS.