Data & AI Scientist for Data Team

Job ID:23002                                                                                                                                         Location: Brussels

Your Impact

The Data and AI Scientist at our client in the fintech industry is pivotal in harnessing the transformative power of data and advanced AI technologies. Their primary mission is to design, develop, and implement both novel and existing machine learning and artificial intelligence models and tools. By deeply understanding our clients core business, its overarching data and AI strategy, and the latest advancements in data science, they aim to significantly enhance our client's decision-making processes. Furthermore, their contributions play a crucial role in elevating the customer experience/value and streamlining operational efficiency, ensuring that our client remains at the forefront of banking innovation and excellence.

Reporting line

The Data & AI scientist reports to the Lead Data Scientist and is part of the data feature team.

Responsibilities

1. Analysis

• Collect, interpret, and analyze both internal and external information to provide actionable insights for various company departments.
• Offer a global view of pertinent information and its current or potential trends to address business challenges, questions, or issues in the banking sector.
• Understand departmental and company needs.
• Develop automated codes (e.g., Python, R, SQL, GraphQL, etc…) for data modeling and statistics.
• Design and automate data visualization tools (PowerBI, Tableau, etc.).
• Provide actionable recommendations based on assessments.
• Initiate the development of data science/Machine Learning algorithms when valuable.

2. Model Development

• Design a range of statistical, machine learning, or AI models, and integrate them to craft data products for improved decision-making, customer experience, and operational efficiency.
• Ensure model creation is rooted in comprehensive research of existing solutions across the banking sector and academia.
• Be able to take and argument 'make-or-buy decisions'
• Evaluate potential machine learning/AI algorithms/Software
• Design machine learning/AI algorithms.
• Integrate various models for optimal results.
• Simplify and present models to relevant departments.

3. Innovation

• Spearhead innovation by perpetually seeking novel data sources, algorithms, and technologies to enhance our clients' data-driven decision-making.
• Continuously explore new data and AI-centric technologies.
• Discover and implement innovative techniques and algorithms to augment customer insights.
• Recommend and incorporate new data sources, collection methods, and advanced algorithms.
• Offer optimization solutions and delivery methods.
• Create partnerships and collaborate with research & development teams, including universities and startups.

4. Stakeholder Management

• Clearly articulate model choices and their utility within product teams and persuade product owners/managers autonomously based on our clients' strategy, industry standards, and available data.
• Suggest analytical solutions for product optimization.
• Oversee product implementation and support its company-wide use.
• Join product and run teams permanently or temporarily to aid in delivering and evolving viable products.

5. Knowledge Management

• Actively monitor market trends in areas such as advanced analytics, machine learning, AI, Big Data, cloud computing, data collection technologies, and Open Data.
• Share acquired knowledge with peers to stay updated with industry advancements and facilitate their practical application in the banking / fintech sector.
• Attend events and training sessions.
• Maintain or develop knowledge resources.
• Promote knowledge exchange with other banking groups.
• Engage in conferences.

6. Management of Analytical Projects and Products

• Oversee analytical projects and technical products, converting business or tech needs into clear deliverables with defined success criteria and KPIs.
• Manage analytical projects and tech program development.
• Ensure delivery of products meeting success criteria within time constraints.
• Join or lead product teams for optimal collaboration.
• Implement AGILE principles in development processes.

7. Coaching and Guidance

• Mentor less experienced data scientists to enhance their expertise in data science and business acumen in the banking sector.
• Take the lead in coaching junior data scientists
• Identify strengths and weaknesses of junior data scientists and design tailored learning paths.
• Ensure the integration of junior data scientists into product teams and maintain team cohesion.

Competencies

• Proficiency in languages like Python, R, SQL, and Java.
• Database Management: Familiarity with databases like MySQL, PostgreSQL
• Big Data Technologies: Familiarity with platforms like Hadoop and Spark.
• Machine Learning and Deep Learning: Understanding of various algorithms and libraries/frameworks like scikit-learn, TensorFlow, and Keras.
• Statistical Analysis: Proficiency in statistical hypothesis testing, regression analysis, and time series forecasting.
• Data Wrangling: Skills in data cleaning, transformation, and ETL processes.
• Predictive Modeling: Ability to develop models for credit scoring, fraud detection, customer segmentation, and other banking-specific use cases.
• Quantitative Analysis: Handling, analyzing, and interpreting complex quantitative data.
• Data Visualization: Proficiency in tools like Tableau, Power BI.
• Industry Knowledge: Understanding of banking operations, products, services, and regulations.
• Risk Management: Knowledge of risk assessment and the ability to integrate risk management into data analyses.
• Business Intelligence: Ability to transform data insights into actionable business strategies.
• Communication: Ability to present findings in a clear and compelling manner to non-technical stakeholders.
• Teamwork: Collaborate effectively with different departments like IT, marketing, and finance.
• Problem-solving: Innovative thinking to approach complex data problems.
• Continuous Learning: The field of data science is rapidly evolving; a commitment to continuous learning is crucial.
• Ethical Judgement: Recognize the ethical considerations related to data privacy, fairness, and biases in modeling.
• Regulatory Compliance: Understanding of financial regulations like Basel III, GDPR, CCPA, and others that affect data use and analytics.
• Fraud Detection: Knowledge of specific methods and tools for detecting financial fraud.
• Customer Analytics: Insights into customer behavior, segmenation, lifetime value, and churn prediction.
• Version Control: Familiarity with tools like Git for managing code and projects.
• Data Warehousing Solutions: Knowledge of tools like Wherescape, Snowflake, Redshift, or BigQuery.

Attitudes

• Innovative Mindset: 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.
• 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

If you are interested, please send your CV to marcel@kazarmaconsulting.eu