Data & AI Analyst (Freelance)

The data & AI analyst for the data & AI team at our client is responsible for understanding business needs, translating them into functional/data requirements, and aiding in the implementation of data and AI products. This role serves as a key bridge between business stakeholders (including Finance, HR Marketing, Legal, Operations teams, etc...) and technical teams, ensuring that data and AI products are designed and implemented to meet business objectives.

In addition, the data & AI analyst is responsible for implementing data governance procedures and providing data coaching to various teams, working in collaboration with risk, compliance, legal, and security teams to make sure that are data products are build according to high quality standards.

Reporting line

The data & AI analyst reports to the product owner of the data & AI team and is part of the data & AI feature team.

Responsibilities

1. Data product delivery (50% of time spent)

• Requirement Analysis: Engage with stakeholders to understand and document their data needs, converting these into specific functional requirements.
• Solution Design: Collaborate with the business teams to formulate solutions that fulfil the functional requirements, focusing on areas like self-service analytics, enterprise dashboarding, embedded analytics and ad hoc analysis.
• Be innovative and think out-of-the-box for new solutions and AI products which use data
• Monitor and test data products to guarantee high quality of the certified data sets and data products
• Lead or contribute to Data Product development projects, making sure solutions are delivered and developed on time and meet quality benchmarks.
• Data Visualization : Create data products such as enterprise dashboards and reports to provide valuable insights and data storytelling

2. Data Governance (20% of time spent)

• Establish and maintain data governance processes to ensure data integrity, privacy, and compliance with legal requirements.
• Document and maintain data models
• Collaborate closely with risk, compliance, legal, and security teams to uphold governance standards.

3. Data Coaching (20% of time spent)

• Provide coaching and mentorship to data consumers to enhance their understanding of data and AI products, data personas, data principles, best practices, and the governance processes.
• Prepare and give trainings as part of our data academy

4. Documentation (10% of time spent)

• Ensure documentation of data sources, processes, and solutions is up-to-date, enhancing accuracy and providing a point of reference for future needs.
• Maintain the documentation system
• Master's Degree or equal by experience (Economy, Management, Commercial Sciences, Psychology, Information Technologies, etc.).
• At least 2 years of relevant experience in a data and/or AI-focused role, including broad experience with Data visualization tools (Tableau and/or Power BI),
• Understand data warehouse and data Lake technologies and coding languages like Dax, R, Python.
• SQL
• Robust analytical skills with the ability to convert business needs into functional requirements.
• Experience in implementing data governance procedures and coaching teams.
• Experience with working Agile and Agile methodologies (SAFe, Scrum, etc...)
• Deep understanding of the business, excellent communication skills, and a strong capability for planning and managing priorities.
• Fluency in English

The ideal candidate will be:

• Innovative Mind-set: Displays a willingness to challenge the status quo and continuously seeks to implement new and improved data technologies and strategies.
• Result-Oriented: Focuses on achieving goals and delivering on commitments, with a high emphasis on the quality of work and the value delivered for the bank.
• Data Advocate: Champions the importance of data governance and educates others about its principles and practices.
• Collaborative: Enjoys working in a team-oriented environment, and understands the importance of sharing knowledge and best practices.
• Adaptive: Shows the ability to adjust quickly to new situations and changing priorities, remaining flexible and efficient in the face of challenges.
• Curious: Maintains an interest in the ever-evolving field of data engineering, eager to learn about new data technologies and practices.
• Ethical: Upholds strong professional ethics, with a focus on data privacy and security regulations.
• Proactive: Takes initiative to anticipate needs, identify potential issues, and propose effective solutions.
• Patient: Understands that data-related tasks can be complex and time-consuming, and does not rush processes at the expense of quality.
• Service-Minded: Keeps the needs of both internal and external customers in mind, and works towards improving their experiences.
• Critical Thinker: Always questioning, never taking data at face value, and using analytical abilities to understand the context behind the numbers.
• Humility: Willingness to admit mistakes, learn from others, and seek help when necessary.
• Pragmatic: practical and hands-on approach to designing, building and maintaining data infrastructure and solutions