Team Lead: Data Science

KEY DUTIES AND RESPONSIBILITES:

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.

REQUIREMENTS

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 Spar
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