What is the role of a Data Scientist?
A data scientist is an analytical expert who will aid in improving the data available for trading analytics to support this decision making as well as collaborating more with other portfolios to gain more value from existing data, and to continue to improve the fundamentals model accuracy. The role involves hands-on delivery of analytics, insight and data science. Technical experience in delivering statistical analytics, data science and insight on large-scale data sets across consumer lead industries is also an imperative. They are responsible for collecting, interpreting and analysing data and will use advanced technology for analytics. They will provide data-driven insights and products to drive commercial value and to enable a wide range of strategic business decision processes. Collaboration with stakeholders is essential throughout the assignment to ensure the insights are actionable and that business are able to deliver them.
Salary Expectations
- Average salary: £46,000
- Low – 26k – High – 182k
Responsibilities:
- Develop data science capability and advocate for the use of data and information assets.
- Grow the test & learn capability of the company to optimise sourcing strategies.
- Provide commercial deep dives into specific business area and translate data into actionable insight & recommendations.
- Develop models (rule-based segmentation, decision trees, predictive models) to help decision-making processes.
- Serve as a subject matter expert on statistical analysis and convey technical results effectively to a variety of audiences, including those with a non-technical background
- Sharing learning and insights across multiple levels and multiple business units
- Present complex ideas and recommendation to various level in the organization
- Apply your machine learning skills and creativity with diverse data to a range of applied drug discovery challenges
- Use your analytical and data science skills to monitor, maintain and improve our large-scale machine learning platform.
- Work closely with engineering and MLOps teams to continuously improve our ability to deliver models and algorithms to project teams.
Tech stacks
- Python e.g. Packages: Numpy, SciPy, Sickt-Learn or Keras or Tensorflow
- Azure or other cloud platforms
- SQL
- Alteryx
Types of data scientist
You can work across a range of areas, including:
- Machine learning scientists
- Strategic data scientists
- Operational data scientists
- Finance
- Retail
- Academia
- Information technology
- Government
- Ecommerce
Qualifications
A degree is often, but not always, required. In many cases, relevant experience in a similar role and understanding of the industry is more highly sought after. A degree in any of these relevant disciplines may help:
- Machine Learning
- Computer Science
- Mathematics
- Physics
- Economics
- Statistics
- Experience in developing Machine Learning or Deep Learning models, methods or algorithms. Experience in working with Data Engineers to get the most out of ETL processes.
Professional development
A professional framework is being developed for this role by the RSS, BCS and Chartered Institute of IT. While working in this role you will build on your technical skills and gain experience in your field. As you move up to more senior roles you will be recruiting, training and leading a team of more junior data scientists. You will be responsible for the business’s data science strategy and test out new and changing technologies for your team/company to use.
The skills you acquire are very broad and can be transferred across to similar job roles and alternative careers.
Alternative careers
- Statistician
- Actuarial scientist
- Mathematician
- Data engineer
- Digital analyst
- Data Analyst