Job Responsibilities
Development of AI and data science projects
- Contribute to the end-to-end development of AI solutions using data science and machine learning, ensuring they address real-world challenges in IoT and Engineering domains, as determined by business.
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.
- Apply 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 exchanging expertise, insights, and lessons learned from past projects.
- Document project details, methodologies, and best practices, ensuring seamless knowledge transfer among team members.
- Mentor and coach junior data science members.
Engineering processes and environment
- Integrate 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.
Desired Experience & Qualification
Education:
- Bachelor's or higher degree in Computer Science (preferred), will consider Engineering or related.
- Additional leadership development an advantage.
Working Experience:
- Minimum of 4 years of relevant experience in AI, data science, and machine learning.
- At least 2 years in a senior data scientist role.
- Strong hands-on experience with AI, Python, DataBricks, PySpark, Azure, SQL, PowerBI, and GIS.
- Success in leading AI/Data Science solutions using data science and machine learning in 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.
- Effective communication and collaboration abilities to work closely with cross-functional teams.
- Detail-oriented mindset with a commitment to delivering high-quality, actionable insights.
Technologies Experience: Working experience in a cross-section of the following technologies (recent 2 years) is required:
- Project Participation: Agile frameworks like Scrum or Kanban (essential).
- Programming: Python (essential), Java or C# (optional).
- AI: AIOps, Generative models such as LLMs (highly desirable), GANs, VAEs, LangChain, 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 required.
Interested?
Please submit your CV through PNet. Kindly acknowledge your application unsuccessful if you do not hear from one of our consultants within two weeks of your application submission.
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