Manager - Data Science

Role Purpose

To develop AI and Machine Learning Models for different business units and have experience in understanding, prioritisation and delivery of data science projects that drive business results and have a passion for discovering solutions hidden in large data sets and working with stakeholders to deliver actionable and impactful insights.

• Build, implement and hand-hold various analytical and modelling initiatives
• Develop custom data models and algorithms to apply to data sets.
• Develop processes and tools to monitor and analyse model performance and data accuracy
• Use predictive modelling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes
• Deliver various predictive and prescriptive analytics using cases, data discovery, data preparation, cleaning, model validation, and evaluation
• Communicate complex quantitative analysis in a concise and actionable manner using data visualisation
• Build models that can make good predictions, evaluate model performance and tune it accordingly
• Work with stakeholders to identify opportunities for leveraging company data to drive business solutions.
• Machine learning, predictive analysis and decision optimization to be applied to real‑world business problems
• Understand, prepare, transform and analyse data to build actionable and impactful data science models that predict emerging trends and provide recommendations

• Mine and analyse data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies
• Assess the effectiveness and accuracy of new data sources and data gathering techniques
• Develop A/B testing framework and test model quality.
• Use multiple techniques for testing and validation of Models and increase and optimize the efficiency
• Coordinate with different functional teams to implement models and monitor outcomes.
• Develop processes and tools to monitor and analyse model performance and data accuracy.

• Identify business needs and objectives to define use case scenarios and user stories
• Gather, clean, and preprocess relevant datasets for model training.
• Annotate data if necessary to ensure quality and relevance.
• Choose appropriate LLM architectures (e.g., GPT-4, BERT, Llama) and train models on prepared datasets.
• Fine‑tune models, including open‑source LLMs like Llama, for specific use cases to enhance performance.
• Develop evaluation metrics (e.g., accuracy, F1 score) and conduct performance testing.
• Perform user acceptance testing (UAT) to ensure models meet business requirements.
• Integrate LLM with existing systems and deploy models in production environments.
• Implement scalable Retrieval‑Augmented Generation (RAG) applications to enhance information retrieval and generation capabilities.
• Document model development processes and create user manuals and training materials.
• Conduct training sessions for end‑users to facilitate adoption and effective use.
• Develop processes and tools to monitor and analyze model performance and data accuracy.

• Conduct comprehensive audits and monitor for proactive management of application performance
• Analyse errors and performance logs using analytic tools to help teams efficiently prevent any issue that may arise
• Investigate server response times by logging and reporting matters such as slow queries, integration responsiveness, or custom code request time.
• Collect metrics of how applications are performing to monitor and troubleshoot runtime issues

• Keep up to date with latest technological innovations and solutions to evaluate impact to business
• Engage in code reviews to review, analyse, improve and share coding skills
• Propose digital innovative solutions which will improve the customer experience, optimize processes through automation, manage medical costs effectively and contribute in the growth of the business.