Data Scientist

Key Responsibilities Data Analytics & Predictive Modelling: Analyse and model large, complex datasets using advanced statistical techniques to derive actionable insights. Utilize machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries to build, test, and deploy predictive models supporting data-driven business strategies. Large Language Models & Generative AI Implement LLM-based applications for processing and interpreting natural language to enhance customer interactions and operational processes. Explore and integrate generative AI solutions to drive innovation and align with business objectives. Automation & Business Process Automation: Design and automate workflows using Power Automate to enhance efficiency and streamline complex business processes. Develop and implement AI-driven automation solutions for real-time decision-making and improved business workflows. Big Data Technologies & Systems Integration Leverage big data technologies such as Hadoop, Spark, and Kafka to process and analyse vast datasets. Utilize data bricks for managing data pipelines and integrating disparate sources for comprehensive analytics. Collaborate with IT and engineering teams to build and optimize system integration frameworks, ensuring high-performance data access and handling. Containerization & Deployment Use Docker and other containerization tools to package applications and manage deployment environments, ensuring consistency and scalability. Collaborate with DevOps teams to implement continuous integration/continuous deployment (CI/CD) pipelines for efficient model delivery. Communication & Collaboration Tools Use Microsoft Teams for effective project management, communication, and knowledge sharing across cross-functional teams. Lead data-driven discussions and present findings to diverse audiences for clarity and engagement. Data Visualization & Reporting Utilize tools like Tableau and Power BI to create impactful dashboards and reports that clearly convey trends and findings to stakeholders. Tailor reporting solutions to assist stakeholders in making informed, strategic decisions. Software Development Life Cycle (SDLC) & Security By Design Engage in all phases of the SDLC, integrating best practices in data management and security by design principles to protect data integrity and confidentiality. Establish documentation, continuous improvement, and risk minimization processes throughout the development lifecycle. Qualifications Bachelor's or master's degree in data science, statistics, computer science, or a related field. Demonstrated expertise with large language models, generative AI, and related applications. Proficiency with programming languages such as Python or R, and experience with tools like SQL, Tableau, Power BI, Data Bricks, Big Data technologies (Hadoop, Spark, Kafka), and Docker. Experience with Microsoft Teams and Power Automate for enhanced workflow and collaboration management. Understanding of systems integration, data warehousing, SDLC, and security-by-design principles. Exceptional analytical, problem-solving, and communication skills, capable of working independently and in teams. Preferred Skills Experience with multi-cloud computing environments (AWS, Google Cloud, Azure) and agile development methodologies. Certifications in data science, analytics, automation, big data, containerization, or security technologies. Proven track record of applying emerging AI technologies to business challenges. Show more Show less