Our client is seeking an experienced AI/ML Specialist to support a range of data science and machine learning initiatives within banking/payments. This 4-6-month project will focus on developing and deploying machine learning models, enhancing data pipelines, and ensuring seamless integration of AI solutions into production environments.

The ideal talent will have expertise in model development, feature engineering, and model evaluation, with a strong background in statistical methods, exploratory data analysis, and data wrangling. Familiarity with MLOps processes, cloud platforms (ideally GCP), and big data tools is essential. Bonus experience in payments, retail, or fraud detection is highly valued.

Key Activities:

• Model Development & Optimisation:
• Build, train, and fine-tune machine learning models (both supervised and unsupervised learning).
• Conduct feature engineering to extract meaningful features from raw data for improved model performance.
• Evaluate models using appropriate metrics (e.g., accuracy, precision, recall).
• Data Science & Analysis:
• Perform exploratory data analysis (EDA) to identify trends, patterns, and outliers in large datasets.
• Prepare and clean data, including handling missing data and scaling features.
• MLOps & Model Deployment:
• Deploy machine learning models into production environments.
• Set up and manage CI/CD pipelines specific to machine learning workflows.
• Monitor model performance post-deployment and establish retraining processes to maintain accuracy over time.
• Technology Utilisation:
• Leverage cloud platforms (preferably GCP) for machine learning services.
• Use distributed data processing tools such as Apache Spark, Hadoop, or Google BigQuery (preferably BigQuery).
• Prototyping & Agile Development:
• Rapidly prototype AI/ML solutions, test, and iterate based on early feedback.
• Work within Agile development frameworks to run proofs of concept (PoCs) effectively.

Your Background:

• Machine Learning Expertise: Proven experience in building, training, and fine-tuning supervised and unsupervised machine learning models, and strong skills in feature engineering and model evaluation.
• Data Science Proficiency: Expertise in statistical methods, exploratory data analysis, and data preparation/wrangling.
• MLOps Experience: Knowledge of CI/CD pipelines, model deployment, and setting up monitoring/retraining processes.
• Technology Skills: Familiarity with cloud platforms like AWS, GCP, or Azure (preferably GCP). Experience with distributed data tools such as Apache Spark, Hadoop, or Google BigQuery (preferably BigQuery).
• Industry Experience (Preferred): Background in payments/banking or retail; fraud-related experience is a bonus.
• Soft Skills: Problem-solving mindset and the ability to approach challenges with innovative solutions. Strong communication skills to work cross-functionally, explain findings, and make recommendations.