Data Scientist
Job Info
Extracts, analyses and interprets complex data sets to provide valuable insights and support data-driven decision-making. This involves applying statistical analysis, machine learning techniques, and data modelling to extract meaningful patterns and predictions from large datasets.
Responsibilities
Work with stakeholders to identify issues and propose data-driven solutions.
Develop algorithms and experiments to manage and extract data for tailored reports.
Use machine learning and statistical techniques to solve problems.
Evaluate data mining models for project suitability.
Clearly communicate data needs and results verbally and in writing.
Create clear reports on customer interactions with the business.
Evaluate data sources and improve collection methods.
Stay updated on technology and methods.
Conduct research and develop prototypes.
Apply insights across departments.
Inspire others about the benefits of data-driven solutions.
Skills & Knowledge
Data Analysis - Proficiency in analysing and interpreting complex datasets to derive actionable insights.
Programming - Strong programming skills in languages such as Python, R, or SQL for data manipulation and analysis.
Machine Learning - Knowledge of machine learning algorithms and techniques for predictive modelling and pattern recognition.
Statistical Analysis - Understanding of statistical concepts and methodologies for hypothesis testing and data validation.
Data Visualisation - Ability to create clear and informative visualisations using tools like Matplotlib, Seaborn, or Tableau to communicate findings effectively.
Data Wrangling - Experience in cleaning, pre-processing, and transforming raw data to ensure accuracy and reliability for analysis.
Problem-Solving - Strong problem-solving skills to tackle complex business challenges using data-driven approaches.
Domain Knowledge - Familiarity with the industry or domain in which they work to contextualise data analysis and insights effectively.
Communication - Excellent communication skills to convey technical concepts and findings to both technical and non-technical stakeholders.
Business Acumen - Understanding of business objectives and processes to align data science initiatives with organisational goals.
Collaboration - Ability to collaborate effectively with cross-functional teams, including business stakeholders, data engineers, and software developers.
Continuous Learning - Willingness to stay updated on emerging technologies, techniques, and methodologies in data science through self-study and professional development opportunities.
Career Progression
Junior Data Scientist - Data Scientist - Senior Data Scientist - Lead Data Scientist - Data Science Manager - Director of Data Science
Career progression may also involve specialisation in specific areas of data science, such as machine learning, natural language processing, or computer vision.
Qualification Pathways
If you are looking to transfer into this role from a related role in the industry, leverage your existing experience and skills in the industry to identify transferable skills that align with a Data Scientist Role. Highlight these transferable skills on your CV and in interviews to demonstrate your suitability for positions within the sector.
If you are new to industry, follow these routes:
Step 1: Obtain a bachelor's degree in a quantitative field such as Computer Science, Statistics, Mathematics, Physics, Engineering, or Economics.
Step 2: Acquire proficiency in programming languages such as Python, R, or SQL, as well as statistical analysis and machine learning algorithms.
Step 3: Seek work experience, graduate programmes or entry-level positions to gain practical experience in data analysis, modelling, and machine learning.
Step 4: Pursue advanced degrees such as a Master's or Ph.D. in Data Science, Statistics, Computer Science, or a related field to deepen knowledge and expertise in data science. You could also consider upskilling with a Level 6 Data Scientist apprenticeship.
Step 5: Consider enrolling in specialised training programmes, workshops, or online courses focused on data science techniques, tools, and methodologies.
Step 6: Obtain relevant certifications such as Microsoft Certified: Azure Data Scientist Associate, or Google Certified Professional Data Engineer to validate skills and enhance career prospects.
Step 7: Stay updated on emerging technologies, techniques, and best practices in data science through self-study, professional development opportunities, and participation in data science communities and events.
Step 8: Build professional networks within the data science community to learn from peers, share knowledge, and explore career opportunities.
Step 9: After 10 years of service, you may be eligible to apply for Fellowship of The Institute of Telecommunications Professionals (ITP).
Other Info
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