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Technical Assistance
1. KEY OBJECTIVES
- Provision of Engineering document preparation, document management, technical assistance, administrative and general support to project team.
- Focal point of project’s document management during Project execution until handover to Operation on Electronic Document Management System (EDMS) administration.
2. KEY ACCOUNTABILITIES
- Provide technical support to all disciplines in the project team as may be requested e.g., generating any spreadsheet/database, registering any project information, prepare TOR or Engineering dossier for particular workshop, etc.
- Scheduling and organizing the meeting, taking minutes of meeting, etc.
- Supporting and maintaining data management and data processing. Manage document filing and retrieval system for reports, correspondence, logs, maps, and figures.
- Provide advisory/training to Project team if any difficulty in using EDMS or any other software.
- Process documents in accordance with Company’s procedures and requirements in a timely, efficient and accurate manner.
- Ensure template of presentation/document/drawing utilize in the project align and update.
- Perform quality checks (e.g., revision number etc. document title and etc.) and validate authenticity of documents before uploading to EDMS.
- Ensure the correct information is timely distributed to the right people.
- Establish and maintain Master Document Registers in cooperation with Project Management Team, and contractors/vendors.
- Communicate with the Project Management Team and contractors/vendors on agreed milestones and systems on the handover and close-out of Project activity to ensure consistency in document control requirements.
- Support as required in any special assignment may have during the execution of project.
3. REQUIREMENTS
- Diploma or above in Secretarial or equivalent (basic degree/diploma, etc.).
- Minimum 5 years document control related experience in oil and gas industry.
- Must have hands-on experience in Microsoft Office which includes Excel, Words, PowerPoint, Corel Draw, and others.
- Possess experience in Engineering or Construction project is preference.
- Good command of spoken and written English.
- Self-motivated and work with minimum supervision.
- Strong organization and coordinating skills.
- Willingness to learn and ability to work in a high-pressure environment.
- Service mind.
Engineer, QA/QC (Civil)
Reporting to: Senior Engineer, QA/QC or Manager, Construction
Division/Department: Lang Lebah Onshore Project Delivery Dept. (LLN)
Location: Initially in Kuala Lumpur and then to Bintulu site
Key Objectives:
Responsible for QA/QC management during site preparation activities of Lang Lebah Project.
Principal Accountabilities:
Engineer, QA/QC (Civil) shall be ensure compliance to Project Specifications and Contract requirements.
Engineer, QA/QC (Civil) shall be responsible for, but not limited to, the following:
- Monitor/Supervise QA/QC activities related to civil works of onshore gas plant to ensure quality, safety and timely completion of the construction works.
- Perform the supervision of civil construction activities such as rock breaking, earthwork, piling, foundation, ground improvement, drainage etc.
- Monitoring the settlement for the areas.
- Review of all certifications/reports for materials such as civil materials, structural steel material.
- Monitor the implementation of Project Quality Plan and Key Performance Indicators/Project Quality Objectives.
- Review of Inspection and Test Plans, Quality Plans, Work Instructions, Method Statement, and various QA/QC procedures
- Ensure that all the procedures and work instructions are effectively implemented during the course of project execution.
- Monitor Non-conformances on regular basis and ensure that NCR is issued, and suitable action is taken timely to implement the corrective action and close the NCR.
- Report any quality issues during construction to Sr QA/QC Engineer and Construction Manager.
- Participate in various audits conducted by Contractor and Company, follow up on audit findings and its closure.
- Coordinate with internal team members such as engineering, construction, HSE, Installation for any interface related to QA/QC.
- Monitor the QA/QC performance of Contractor, take up with Contractor on quality issues and attend regular QA/QC meetings.
- Liaise with Senior QA/QC Engineer on all quality issues and report regularly on quality performance of Contractor.
- Follow up and verify As Built and Final documentation.
Qualifications And Experience:
- Bachelor’s degree, or equivalent, in civil / structural engineering or equivalent professional qualification in related field.
- Professional certificate by accredited Bodies/ Associations is an advantage but not necessary.
- Minimum of 20 years’ experience in site preparation activities for onshore Oil & Gas facilities.
- Must have worked previously with Oil and Gas Operating Company or contractors involved in civil construction.
- Desired Competency (Skills and Abilities)
- In depth knowledge of earthwork, piling, foundation, drainage, pavement, concreting, roadwork, structural work.
- Thorough knowledge of compaction testing, cube test and all related testing pertaining to civil and structural work.
- Must possess strong experience in managing civil QA/QC activities for onshore gas / refinery / petrochemical units.
- Must be well versed in painting, coating, fireproofing, insulation and other related activities for structures, buildings, and shelters.
- Must be well acquainted with construction of access roads, piling, buildings, drainage system for onshore gas facility.
- Must be well conversant with international code and standards such as ASTM D1557, ASTM D6913, ASTM C150, ASTM C192 etc.
- Should be acquainted with local Malaysian regulations related to QA/QC/Inspection of Oil & Gas facility.
- Good communication, coordination and organizing skills with ability to work with minimal supervision and liaise effectively and efficiently with other team members and outside stakeholders.
- Demonstrate a high level of interpersonal skills and integrity.
MOBILE APPS DEVELOPER
A Mobile App Developer is responsible for the complete development lifecycle of mobile applications, from conceptualization to deployment and maintenance. This role involves technical proficiency, collaboration, and a commitment to delivering high-quality, user-centric mobile experiences.
- App Development:
- Design and develop mobile applications for specific platforms (iOS, Android, or cross-platform) using appropriate programming languages (Swift, Kotlin, Java, React Native, Flutter, etc.).
- Ensure code quality, performance, and scalability of the application while adhering to best practices and design patterns.
- Collaboration & Communication:
- Work closely with designers, product managers, and other developers to understand project requirements, provide technical insights, and align on development goals.
- Participate in sprint planning, stand-ups, and regular team meetings to discuss progress, challenges, and solutions.
- UI/UX Implementation:
- Translate UI/UX designs into functional user interfaces, ensuring a seamless and visually appealing experience for end-users.
- Implement animations, transitions, and interactive elements to enhance user engagement.
- Testing & Debugging:
- Conduct thorough testing of the mobile application, perform unit tests, and troubleshoot issues or bugs to ensure a smooth user experience across devices and platforms.
- Collaborate with QA teams to carry out testing processes including functional, regression, and usability testing.
- Performance Optimization:
- Optimize app performance by analyzing and improving code efficiency, minimizing load times, and optimizing memory usage.
- Implement caching mechanisms and optimize network requests for faster data retrieval.
- Security & Compliance:
- Implement security measures to protect user data and ensure compliance with privacy regulations and industry standards.
- Conduct security audits and apply encryption techniques where necessary.
- Documentation & Maintenance:
- Create and maintain technical documentation including code comments, architecture diagrams, and version history to facilitate future updates and maintenance.
- Provide ongoing support, bug fixes, and updates for released applications, ensuring they remain compatible with new OS versions and devices.
- Continuous Learning & Improvement:
- Stay updated on the latest mobile development trends, tools, and technologies to enhance skills and contribute to continuous improvement within the development team.
- Engage in personal development activities, attend workshops, and share knowledge with colleagues.
Responsibilities:
- Mobile App Development:
- Design and develop mobile applications for iOS and/or Android platforms using programming languages such as Swift, Objective-C (for iOS) or Kotlin, Java (for Android).
- Implement best practices in mobile application development, ensuring high performance, responsiveness, and scalability.
- Cross-Platform Development (Optional):
- Utilize cross-platform development frameworks like React Native, Flutter, or Xamarin to build applications that can run on multiple platforms with a single codebase.
- Collaboration & Communication:
- Work closely with designers, product managers, and other developers to understand requirements, user stories, and technical considerations for mobile app development.
- Collaborate on API design, integration, and data handling to ensure seamless communication between the app and server-side components.
- UI/UX Implementation:
- Translate design mockups and wireframes into functional and visually appealing user interfaces, ensuring adherence to design guidelines and best practices.
- Optimize user interface elements for performance and user experience on mobile devices.
- Testing & Debugging:
- Conduct thorough testing of mobile applications, identify and resolve issues or bugs, and ensure the app's compatibility across different devices and OS versions.
- Perform unit testing, integration testing, and assist in user acceptance testing (UAT) to ensure the app meets quality standards.
- Security & Compliance:
- Implement security measures and best practices to safeguard user data and ensure compliance with relevant industry standards and regulations (e.g., GDPR, HIPAA).
- Documentation & Maintenance:
- Create technical documentation, including code comments and system diagrams, to facilitate maintenance and future updates.
- Provide ongoing support, maintenance, and enhancements for existing mobile applications.
Qualifications:
- Bachelor’s degree in Computer Science, Software Engineering, or related field (or equivalent practical experience).
- Proficiency in mobile app development languages and frameworks (e.g., Swift, Kotlin, React Native, Flutter).
- Strong understanding of mobile UI/UX principles and best practices.
- Experience with version control systems (e.g., Git), API integration, and third-party libraries.
- Ability to work collaboratively in a team environment and communicate technical concepts effectively.
AI/ML SPECIALIST
- Problem Definition: Work closely with business stakeholders to understand their needs and challenges and define how AI/ML solutions can address these issues. This involves translating business problems into machine learning problems.
- Data Collection and Preparation: Identify and gather relevant data for training machine learning models. This may involve working with data engineers to ensure data quality, cleaning, and preprocessing to make it suitable for analysis.
- Model Development: Design and develop machine learning models that are appropriate for the given problem. This includes selecting algorithms, fine-tuning parameters, and optimizing models for performance.
- Training and Evaluation: Train machine learning models using historical data and evaluate their performance using metrics relevant to the problem at hand. Iteratively refine models to improve accuracy and effectiveness.
- Feature Engineering: Identify and create relevant features from raw data that can enhance the performance of machine learning models. This involves a deep understanding of the data and the problem domain.
- Algorithm Selection: Choose the most suitable machine learning algorithms based on the nature of the data and the goals of the project. This may involve experimenting with various algorithms to find the best fit.
- Model Deployment: Implement models into production environments, collaborating with DevOps and IT teams to ensure seamless integration. Monitor and maintain deployed models to ensure ongoing performance.
- Collaboration: Work collaboratively with cross-functional teams, including data engineers, software developers, and domain experts. Effective communication is crucial for understanding requirements, explaining model outputs, and iterating on solutions.
- Continuous Learning: Stay abreast of the latest developments in AI/ML, including new algorithms, techniques, and tools. Continuously update and expand knowledge to apply cutting-edge solutions to business problems.
- Ethical Considerations: Be aware of ethical considerations related to AI/ML, such as bias in algorithms and the responsible use of data. Ensure that AI solutions align with ethical standards and industry best practices.
- Documentation: Document the entire machine learning process, including data sources, model architecture, parameters, and results. Clear documentation is essential for reproducibility and knowledge transfer within the team.
- Problem Solving: AI/ML specialists need to be adept at problem-solving. They often encounter challenges related to data quality, model performance, and real-world implementation, requiring creative and analytical problem-solving skills.
Responsibilities:
- Collaborate with stakeholders to understand business challenges and identify opportunities where AI/ML can provide solutions.
- Define clear and achievable goals for AI/ML projects.
- Identify and acquire relevant datasets for training and testing machine learning models.
- Analyse data to understand patterns, trends, and potential challenges.
- Clean and preprocess data to ensure quality, consistency, and suitability for machine learning tasks.
- Handle missing data, outliers, and other data quality issues.
- Identify and create relevant features from raw data to enhance model performance.
- Utilize domain knowledge to extract meaningful features.
- Select appropriate machine learning algorithms based on the nature of the problem and data.
- Develop and train machine learning models using tools like TensorFlow, PyTorch, or scikit-learn.
- Fine-tune model hyperparameters to optimize performance.
- Conduct experiments to find the best combination of parameters.
- Evaluate the performance of machine learning models using relevant metrics.
- Validate models on different datasets to ensure generalizability.
- Deploy machine learning models into production environments.
- Collaborate with DevOps and IT teams to integrate models into existing systems.
- Implement monitoring systems to track model performance in real-time.
- Address issues related to model degradation and adapt models to changing data distributions.
- Collaborate with cross-functional teams, including data engineers, software developers, and domain experts.
- Communicate effectively to ensure a shared understanding of project goals and requirements.
- Stay up to date with the latest developments in AI/ML.
- Experiment with new algorithms and technologies to enhance skillset.
- Be aware of ethical implications in AI/ML, including bias and fairness concerns.
- Ensure that AI solutions adhere to ethical standards and regulations.
- Document the entire machine learning pipeline, including data sources, preprocessing steps, model architectures, and deployment details.
- Provide clear documentation for knowledge transfer and reproducibility.
- Present findings and results to non-technical stakeholders in a clear and understandable manner.
- Communicate progress, challenges, and recommendations to project teams and management.
- Apply creative and analytical problem-solving skills to address challenges in data, models, or implementation.
- Ensure that AI solutions comply with security and privacy standards.
- Implement measures to protect sensitive data used in machine learning projects.
Qualifications
- Bachelor's degree in a relevant field such as Computer Science, Data Science, Machine Learning, Statistics, or a related discipline.
- Proficiency in programming languages commonly used in AI/ML, such as Python or R.
- Solid understanding of machine learning frameworks and libraries, such as TensorFlow, PyTorch, scikit-learn, or others.
- Experience with data manipulation and analysis tools, like Pandas and NumPy.
- Strong knowledge of statistical concepts and methods.
- Demonstrated experience in developing and implementing machine learning models.
- Knowledge of various machine learning algorithms and their applications.
- Ability to select and fine-tune models based on the specific requirements of a project.
- Experience in data collection, cleaning, and preprocessing.
- Knowledge of database systems and data storage solutions.
- Strong analytical and problem-solving skills to address challenges in data and model development.
- Ability to translate business problems into machine learning problems.
- Solid understanding of mathematical concepts relevant to machine learning, such as linear algebra, calculus, and probability.
- Proficient in software development practices, including version control, code review, and collaborative coding.
- Understanding of best practices for writing maintainable and scalable code.
- Effective communication skills to convey complex technical concepts to both technical and non-technical stakeholders.
- Ability to collaborate with cross-functional teams.
- Experience in managing and coordinating machine learning projects, including planning, execution, and delivery.
- Demonstrated commitment to staying current with advancements in AI/ML.
- Ability to adapt to new tools, technologies, and methodologies.
- Depending on the industry, domain knowledge relevant to the organization's business can be beneficial.
- Awareness of ethical considerations in AI/ML, including bias, fairness, and privacy concerns.
- Understanding of ethical guidelines and regulations related to AI.
- Practical experience in applying machine learning to real-world problems through internships, projects, or work experience.
- Relevant certifications in machine learning or data science from reputable organizations can be an added advantage.
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DATA ANALYST |
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Tasks typically involve collecting, analysing, and interpreting data to help businesses make informed decisions. Here are some common tasks and targets of a data analyst:
1. Data Collection: Gathering data from various sources such as databases, spreadsheets, surveys, or online platforms. This may involve designing data collection methods and ensuring data quality and integrity. 2. Data Cleaning and Preprocessing: Identifying and addressing any errors, inconsistencies, or missing values in the data. This step may also involve transforming data into a suitable format for analysis. 3. Exploratory Data Analysis (EDA): Conducting initial data exploration to understand the patterns, relationships, and distributions within the data. This may involve using statistical techniques, data visualization tools, and summary statistics. 4. Data Modelling and Analysis: Applying statistical techniques and mathematical models to analyse the data. This could include regression analysis, clustering, classification, time series analysis, or predictive modelling. 5. Data Visualization: Creating visual representations of data to effectively communicate insights and findings. This includes using charts, graphs, and dashboards to present information in a clear and concise manner. 6. Reporting and Presentation: Summarizing and documenting analysis results in reports or presentations for stakeholders. Presenting findings in a way that is understandable to non-technical audiences. 7. Data-driven Decision Making: Collaborating with decision-makers and stakeholders to understand their requirements and provide actionable insights based on data analysis. Helping organizations make informed decisions and optimize processes. 8. Data Monitoring and Maintenance: Continuously monitoring data quality, identifying anomalies, and updating analysis as new data becomes available. Ensuring the accuracy and reliability of ongoing data processes.
The target of a data analyst is to provide meaningful insights and actionable recommendations based on data analysis to support business goals, improve decision-making processes, identify opportunities for improvement, optimize operations, and enhance overall performance. It's important for data analysts to have a solid understanding of statistical methods, programming languages (such as Python or R), data visualization tools (like Tableau or Power BI), and database querying languages (such as SQL). Strong analytical and critical thinking skills, attention to detail, and effective communication abilities are also crucial for success in this role.
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DIGITAL PROJECT ENGINEER |
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As a Digital Project Engineer, tasks and targets will revolve around planning, managing, and executing digital projects to ensure their successful completion. You will be required to travel to vendor sites and offshore locations for project implementation. Here are some specific tasks and targets you might encounter in this role.
Tasks: 1. Project Initiation: • Collaborate with stakeholders to define project scope, objectives, and requirements. • Conduct initial feasibility studies to assess the project's viability and potential challenges. 2. Project Planning: • Develop detailed project plans, including timelines, milestones, and deliverables. • Allocate resources (human, financial, technical) to tasks and project phases. • Create risk assessment and mitigation strategies. 3. Team Coordination: • Assemble cross-functional teams comprising developers, designers, testers, and other relevant roles. • Facilitate communication among team members and ensure clear roles and responsibilities. 4. Technical Oversight: • Provide technical guidance and expertise to the development team. • Make decisions regarding technology stack, architecture, and development methodologies. 5. Execution and Monitoring: • Ensure that the project is progressing according to the established schedule and milestones. • Monitor team productivity, identify bottlenecks, and implement solutions. 6. Quality Assurance: • Implement quality control processes to ensure deliverables meet defined standards. • Conduct testing and validation of software components or digital assets. 7. Client/Stakeholder Communication: • Maintain regular communication with clients and stakeholders to provide updates on project progress. • Address concerns, answer queries, and manage expectations. 8. Issue Resolution: • Identify and address project issues and roadblocks promptly. • Collaborate with teams to find effective solutions and minimize disruptions. 9. Change Management: • Evaluate change requests and assess their impact on project scope and timeline. • Make informed decisions regarding accepting or rejecting changes. 10. Documentation: • Ensure comprehensive documentation of project requirements, designs, codebase, and processes. • Create user manuals, technical documentation, and any necessary guides.
Targets: 1. Timely Delivery: • Deliver the project within the established timelines and milestones. 2. Quality Assurance: • Ensure the project meets high-quality standards and is free from critical bugs or issues. 3. Stakeholder Satisfaction: • Maintain positive relationships with clients and stakeholders by meeting their expectations. 4. Budget Adherence: • Manage project expenses to stay within the allocated budget. 5. Effective Team Collaboration: • Foster a collaborative team environment and ensure efficient communication. 6. Risk Management: • Identify and mitigate potential risks to prevent project delays or failures. 7. Documentation Completeness: • Create thorough and organized documentation for reference and future maintenance. 8. Adaptability: • Successfully navigate changes in project scope or requirements while minimizing disruptions. 9. Innovation and Improvement: • Introduce innovative ideas and best practices to enhance project processes and outcomes. |
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COMPANY: PETRONAS
POSITION: EXECUTIVE (HSE COMMUNICATION)
DURATION: 1 JANUARY 2024 – 14 DECEMBER 2024
LOCATION: LEVEL 20 (INSIDE BUSTLE) TOWER 1 KLCC
KEY RESPONSIBILITES:
- Syndicating with internal and external stakeholders to gather input on topics and focus areas requiring strategic communications approach.
- Identifying short- and long-term communication opportunities to promote HSE Culture and support organizational learning.
- Establishing the annual communications plan
- coordinating communication series and assisting with engagements using a wide range of communication tactics and platforms
- Manage and oversee the daily operations of all HSE communications channels and ensuring consistent alignment with strategic communications initiatives and keeping abreast with the latest trends or technology in delivering start of the art and effective communications.
REQUIREMENT:
- Preference for bachelor’s degree in business & management / Management Information System / Technology, Public Relations, Graphic Design, Interactive Media, Media Digital, Mass Communications or equivalent.
- At least 5 years relevant experience in corporate communications, culture change and transformation or equivalent.
- Experience in some aspect of Change Management, understanding of
- Upstream Business, Behavioural Psychology and skill in graphic design are an added advantage.
APPENDIX 1-1: DETAIL JOB DESCRIPTION AND QUALIFICATION REQUIREMENT
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DATA SCIENTIST |
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TASK & TARGETS |
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The tasks and targets as a data scientist is typically revolve around leveraging data to extract insights, build predictive models, and support data-driven decision-making. Here are some common tasks and targets associated with the role of a data scientist:
Tasks: 1. Data Exploration and Preparation: Collecting, cleaning, and preprocessing data to ensure its quality, integrity, and suitability for analysis. 2. Exploratory Data Analysis (EDA): Conducting statistical analysis and visualization techniques to understand data patterns, relationships, and anomalies. 3. Feature Engineering: Selecting or creating relevant features from raw data to improve model performance and predictive accuracy. 4. Model Development and Evaluation: Applying machine learning algorithms and statistical techniques to develop predictive models, assess their performance, and iterate as needed. 5. Model Selection and Optimization: Comparing and selecting the most suitable models based on evaluation metrics and optimizing hyperparameters to improve model performance. 6. Deployment and Integration: Collaborating with software engineers or data engineers to deploy models into production systems and integrate them into operational workflows. 7. Model Interpretability: Explaining and interpreting model predictions to stakeholders, ensuring transparency and building trust in the model's outcomes. 8. Monitoring and Maintenance: Monitoring the performance of deployed models, assessing their accuracy over time, and performing necessary updates or retraining. 9. Collaboration and Communication: Collaborating with cross-functional teams, such as business stakeholders, data engineers, and domain experts, to understand requirements, align on goals, and communicate findings effectively.
Targets: 1. Predictive Model Accuracy: Achieving a specific level of accuracy or performance metrics (e.g., precision, recall, F1-score) for predictive models, based on the problem domain and business requirements. 2. Data Processing Efficiency: Implementing efficient data processing and feature engineering pipelines to handle large volumes of data and optimize computational resources. 3. Time-to-Insights: Delivering timely insights and recommendations to stakeholders, enabling informed decision-making within specific time frames. 4. Business Impact: Demonstrating the value and impact of data science projects on key business metrics, such as revenue, cost savings, customer satisfaction, or operational efficiency. 5. Model Deployment and Integration: Successfully deploying models into production environments, ensuring seamless integration with existing systems and workflows. 6. Model Monitoring and Maintenance: Continuously monitoring model performance, detecting, and addressing degradation or anomalies, and maintaining model accuracy and relevancy over time. 7. Collaboration and Teamwork: Contributing effectively to cross-functional teams, collaborating with stakeholders, and fostering a culture of data-driven decision-making within the organization. 8. Professional Development: Keeping up to date with the latest advancements in data science, machine learning, and statistical modelling techniques, and continuously improving skills and knowledge. It's important to note that the specific tasks and targets of a data scientist may depend on the organization, and project requirements. Data scientists often work closely with stakeholders to define specific targets that align with business goals and objectives.
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