Data Science Lead Centurion - Centurion
Outsource SA Solutions Ltd
Leadership Collaborate closely with the data science manager to establish and execute the team's technical vision, strategy, and goals. Provide effective leadership to the data science team, fostering a collaborative and innovative environment that encourages creativity and growth. Guide and mentor team members, promoting their professional development and enhancing their skills in AI, data science, machine learning, and related technologies. Effectively manage and allocate resources, ensuring efficient project execution, and alignment with business goals. Continuously align delivery to the company and Development & Engineering team strategy and planning. Delivery of AI and Data Science solutions Lead and contribute to the end-to-end development of AI solutions using data science and machine learning, ensuring they address real-world challenges in telemetry, IoT, and Engineering domains, as determined by business. Ensure delivery of solutions on time, in budget, with the desired functionality, at the defined quality level in a sustainable way. Collaborate closely with cross-functional teams to gather requirements, design solutions, and integrate insights into operational processes. In collaboration with field experts, coordinate the design and implementation of generative models, contributing to enhanced vehicle tracking, recovery operations, and operational efficiency. Best practice quality and testing Uphold the highest standards of quality in AI and Data Science solutions by implementing best practices in data preparation, model development, validation, data quality, etc. Oversee rigorous testing methodologies to validate the accuracy, reliability, and effectiveness of developed models, ensuring they meet business requirements. System maintenance and support Take responsibility for the ongoing maintenance and optimization of AI / DS products, services, models, and solutions, ensuring they remain effective and aligned with changing business needs. Collaborate with the IT and other engineering teams to address any technical issues, ensure system stability, and provide timely support when required. Knowledge transfer Foster a culture of knowledge sharing within the team by promoting the exchange of expertise, insights, and lessons learned from past projects. Facilitate the documentation of project details, methodologies, and best practices, ensuring seamless knowledge transfer among team members. Mentor and coach junior data science members. Engineering processes and environment Drive the integration of AI solutions into the overall engineering environment, leveraging tools and processes that align with industry best practices. Collaborate with engineers, data engineers, and IT specialists to ensure the scalability, reliability, and security of AI solutions in a production environment. Strive to enhance engineering processes and practices by contributing insights from AI projects to improve the overall operational efficiency of the organization. Effective use of AI and Data Science development toolsets. Follow department development standards as adapted to Data Science. Education: BSc (Comp Sci) (completed) or BEng (completed) or higher qualification of relevance. Other STEM degrees from established Universities (Computers, Math, Stats, IT or BCom-IT) might be considered given relevant minimum experience. Additional leadership development an advantage. Working Experience: Minimum 4 years hands-on experience: Minimum of 4 years of relevant experience in AI, data science, and machine learning At least 1 years in a team lead or managerial role, and 2 years in senior data scientist role. Established hands-on experience with Python, DataBricks, PySpark, Azure, SQL, PowerBI, and GIS. Success in leading AI / Data Science solutions using data science and machine learning in telemetry, IoT, Software Development or Engineering contexts. Familiarity with Linux and proficiency in additional programming languages like Java or C#. Excellent analytical and problem-solving skills, Ability to translate business needs into data-driven solutions. Strong leadership skills with the ability to motivate and guide a team toward achieving common goals. Effective communication and collaboration abilities to work closely with cross-functional teams. Detail-oriented mindset with a commitment to delivering high-quality, actionable insights. Experience in the Telematics or automotive industry is a plus. Technologies Experience: Working experience in a cross-section of the following technologies (recent 2 years) is required: Project Management: Agile frameworks like Scrum or Kanban (essential), Project management tools Programming: Python (essential), Java or C# (optional) AI: AIOps, Generative models such as LLMs (highly desirable), GANs, VAEs, LangChain, etc. ML: MLOps, Scikit-learn, Keras, TensorFlow, PyTorch, XGBoost, LightGBM, CatBoost Data: DataBricks (essential), PySpark (essential), SQL (essential), Jupyter Notebooks, pandas, etc. Big Data: MS Azure (essential), Apache Hadoop, Delta Lake format, Parquet, and Spark Visualization: PowerBI, Matplotlib, Seaborn, Plotly Version Control: Git (essential), GitHub, GitLab, or related. Geospatial: GIS software, GeoPandas Deployment: Docker and Kubernetes, CI/CD pipelines Testing: Unit and integration testing frameworks, Model performance monitoring and logging tools Documentation: MS Office, Wiki platforms (for team documentation) Maths: Statistical techniques and hypothesis testing, University level mathematics
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