YOU TASKS AND RESPONSIBILITES


Identify opportunities to develop statistical insight, reports and models to support organizational objectives, while collaborating across the organization effectively
Critique statistical analyses
Use a variety of data analytics techniques (such as data mining and prescriptive and predictive analytics) for complex data analysis through the whole data life cycle
Use model outputs to produce evidence and help design services and policies
Understand a broad range of statistical tools, particularly those deployed within the organization, and can use these appropriately and help others to use them
Help to identify the data engineering requirements for any data science product, while working with data engineers and data scientists to design and deliver those products into the organization effectively
Understand the need to cleanse and prepare data before including it in data science products and can put reusable processes and checks in place
Understand a broad range of architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organization, and can use these tools appropriately and help others use them
Be a leader in the data science space
Demonstrate in-depth knowledge of data science tools and techniques, which you can use to solve problems creatively and to create opportunities for the team
Act as a coach, inspiring curiosity and creativity in others
Demonstrate in-depth knowledge of your chosen profession and keep up to date with changes in the industry
Challenge the status quo and always look for ways to improve data science
Lead and support the organization by using data science to create change
Identify opportunities to develop data science products to support organizational objectives, while collaborating across the organization to fulfil goals
Show an understanding of the role of user research, and can design and manage processes to gather and establish user needs
Communicate relevant and compelling stories effectively and present analysis and data visualizations clearly to get across complex messages
Work with colleagues to implement scalable data science products, and to understand maintenance requirements
Manage a continuous development plan and link to learning objectives and organizational goals
Support data science capability building across the team and wider organization
Confidently talk about the benefits of data science approaches to existing and potential customers
Demonstrate a good understanding of a range of data science techniques, such as machine learning and natural language processing, and use them to build data science solutions, including reports, models and dashboards
Demonstrate an understanding of how ethical issues fit into a wider context and can work with relevant stakeholders
Stay up to date with developments in data ethics standards and legislation frameworks, using these to improve processes in your work area
Identify and respond to ethical concerns in your area of responsibility
Write complex programs and scripts
Seek to make code open source where appropriate
Supervise junior analysts and set coding standards for your team
Understand software architecture and how to write efficient, optimized code
Perform user testing on products prior to launch
Demonstrate an understanding of the differences between delivery methods, such as Agile and waterfall, and can choose the most appropriate method to deliver each product
Define the minimum viable product (MVP) and support decisions about priorities
Work with specialists in multidisciplinary teams to smoothly deliver data science products into the organization


WHO YOU ARE


Master’s or Ph.D. in Computer Science, Statistics, Mathematics, Data Science, or a related field or Bachelor’s with minimum 5 years experience.
Strong academic background with coursework or research in machine learning, AI, statistical modeling, and data analysis.
Proficiency in programming languages such as Python or R for data analysis and machine learning.
Familiarity with libraries and frameworks like TensorFlow, PyTorch, scikit-learn, pandas, and NumPy
Advanced knowledge of statistical methods and techniques
Experience in hypothesis testing, regression analysis, clustering, and classification.
Expertise in machine learning algorithms and techniques
Familiarity with big data technologies and frameworks (e.g., Hadoop, Spark)
Proficiency in SQL, relational database, and NoSQL databases.
Strong analytical and problem-solving skills.
Ability to evaluate assumptions and limitations of data.
Excellent verbal and written communication skills.
Experience working collaboratively in interdisciplinary teams.
Demonstrated experience in leading and mentoring other data scientists
Ability to align data science initiatives with business objectives.
Strong decision-making skills.


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