Data Scientist

Who are we looking for?

You enjoy working on complex data problems whilst being able to suggest simple (yet effective) solutions. You are comfortable working with uncertainty and like to make things clearer. You’re passionate about technology and keep up as it evolves. You focus on the future and thrive most when solving problems. Client’s love working with you. You are honest and do things when you say you will, you also know how to explain things clearly and concisely. You can educate and inspire. You’ve got a background in data science, machine learning algorithms, data analytics, and data engineering along with their technologies. You’re equally comfortable presenting to clients, providing advice or building prototypes. You’re a collaborator and enjoy stepping out of your role from time to time, whether it’s to support your clients, colleagues or to learn something new.

Key Responsibilities

• Leading client projects and providing subject matter expertise.
• Working in scrum-like environments for iterative and ‘fail-fast’ work and innovation.
• Working in cross-disciplinary teams.
• Working at the PoC (proof-of-concept) stage through to MVP and MMP stages.
• Assessing your clients’ business and technical needs with the ability to identify opportunities for data science to be used. Managing client and stakeholder relationships appropriately.
• Solving problems using data science techniques and in a scientifically robust fashion.
• Identifying data sources that are relevant to client needs, and related data science concepts that leverage those sources to aid the client.
• Working with various forms of data (e.g., unstructured, semi-structured or structured; text, time-series or image) and suitably modelling them (e.g., table, key-value pair, graph) for efficient data science use.
• Investigating and analysing data to see ‘the wood from the trees’ and drilling down to the ‘whys’ of the data.
• Applying statistical and evidence-based techniques to inform insights and actions from the data.
• Communicating technical content at the right pitch/level both internally and to customers.
• Presenting to the client, using data science tooling and investigation, a ‘story’ of the data.
• Building maintainable code that use existing data science libraries, implement existing data science techniques, or implement novel techniques.
• Designing, evaluating, and implementing on-premise, cloud-based and hybrid data science and machine learning techniques and algorithms (including providing relevant review and guidance on testing aspects, identification of risks and proposing and implementing their mitigations).
• Developing scalable models and algorithms that can be deployed into production environments.
• Applying ethical principles in handling data.
• Accurately delivering high quality work to agreed timelines and taking the initiative and knowing how to jump straight in.
• Supporting client engagements, including pitches and presentations.
• Helping to support & grow Daintta by actively inputting into the company strategy and helping to shape our future.
• Representing us and our core values: transparent, fair and daring