Total 1,147 data entry job vacancies in Information Technology category

DSBJ is a leading worldwide technology partner providing critical components for IoT intelligence, with a special focus on Telecommunication Equipment and Metal Precision, LED technologies and Interconnect solutions. Technological innovation is in the DNA of entire organization and is also the cornerstone of DSBJ’s success. With cutting-edge production facilities and advanced technology, we constantly deliver state-of-the-art products and quality services. Towards this, DSBJ has established the AI COE team to fully incorporate Big data and Artificial Intelligence into strategic roadmap and enable digital transformation and smart manufacturing/engineering initiatives.

As the Senior / Principal Data Scientist specializing in Smart Engineering, she/he will apply advanced analytics—including machine learning, generative AI, and optimization—within engineering and design domains to convert complex engineering data into actionable insights that drive faster, higher-quality decisions. This role will design, build, validate, and deploy analytical prototypes, taking solutions from concept through production. Working closely with experts in big data, software engineering, and advanced analytics, she/he will also contribute to the development of scalable, cutting-edge analytics capabilities that support evolving business requirements and product innovation.

Main Responsibilities:

• Design and develop algorithms to parse, interpret, and analyse engineering data, inclusive of structured data, technical PDF, 2D drawing, 3D CAD models
• Apply machine learning, deep learning, and geometric modelling algorithms to engineering problems, including drawing understanding (symbol, dimension, feature recognition), CAD feature classification and semantic labelling, part similarity, clustering, and reuse detection
• Collaborate with software and data engineers to productionize models and deploy them as services or internal platforms
• Work closely with mechanical engineers, manufacturing engineers, and process design engineers to translate domain knowledge into AI features
• Communicate advanced analytics solutions to non-technical stakeholders, translating technical insights into clear business value to drive understanding and adoption
• Advise management and stakeholders on best practices for applying advanced analytics to engineering problems through exploration, benchmarking, and evidence-based evaluation
• Work with project manager to break down overall work into manageable and tractable tasks while satisfying time, cost and resource constraints.
• Perform progress check, solution review and documentation on regular basis.

Job Requirements:

• At least Bachelor degree in Mechanical Engineering, Data Science, Applied Mathematics, Statistics, Computing Science. A post-graduate degree is an advantage
• Minimum of 5 years of work experience in academic or business context to apply Data Science, Machine learning, Deep Learning, Optimization to engineering problem
• Profound knowledge (e.g. application, limitation, challenges) of advanced analytics (e.g. rule-based, deep learning, LLM) in analysing the engineering data (e.g. PDF, DXF, STEP, ODB++)
• Hands-on modelling/coding experience using various tools/libraries (e.g. AutoGluon, Ultralytics, Gurobi, TensorFlow, PyTorch, SciKit-Learn, Open CASCADE)
• Proficiency in programming language (e.g. Python, Java) and database querying (e.g. SQL)
• Self-motivated, proactive, independent learner who is willing to learn, experiment, and share knowledge with broader AI COE team to build innovative culture.
• Proficiency in both English and Chinese, to liaise with the Chinese-speaking associates in China Headquarter office.
• Domain knowledge in engineering design, industrial automation and engineering software is highly valued
• Experience in feature recognition, design intent extraction, or DFM/DFA automation is highly valued

Responsibilities:

• Lead and collaborate with data scientists and analysts to drive innovation, knowledge sharing, and continuous improvement.
• Design, develop, and deploy robust analytical and machine learning models using advanced statistical techniques.
• Partner with business and technical stakeholders to identify key challenges and translate them into data-driven solutions.
• Develop dashboards and data visualisations to effectively communicate insights to technical and non-technical audiences.
• Stay current with emerging data science trends, tools, and methodologies, applying relevant advancements where appropriate.
• Contribute to the establishment and enhancement of data governance, data quality, and analytics best practices.

Requirements

• Master’s degree or PhD in Data Science, Statistics, Machine Learning, Computer Science, or a related quantitative discipline.
• Significant hands-on experience in data science or advanced analytics roles, preferably within complex engineering or operational environments.
• Strong proficiency in Python and/or SQL, with experience using machine learning libraries and frameworks.
• Demonstrated ability to manage multiple projects concurrently and deliver high-quality outcomes in a fast-paced setting.
• Strong analytical and problem-solving skills, with the ability to interpret and extract insights from complex datasets.
• Excellent communication and stakeholder engagement skills, with the ability to explain technical concepts clearly to non-technical audiences.
• Familiarity with enterprise systems (e.g. PLM, MES, MRO) and cloud platforms such as AWS will be advantageous.

To submit your application, please apply online to apply@rn-care.com or call +(65) 6513 5697 for private and confidential discussion.

Your interest will be treated with the strictest of confidence. We regret that only shortlisted candidates will be notified or contacted.

RN Care Pte. Ltd
EA License No: 17C8900

The Data Scientist works closely with business stakeholders to uncover challenges, refine use cases, and design analytical solutions that drive measurable outcomes. This role spans the entire data science lifecycle—problem framing, data preparation, modelling, evaluation, communication of insights, and enabling adoption through compellingvisualizations and decision‑support tools.

Key Responsibilities

1. Problem Definition & StakeholderEngagement

• Facilitate discussions with business users to understand their pain points, clarify objectives, and convert them into analytically sound data science problem statements.
• Collaborate with stakeholders throughout the project to refine hypotheses, validate findings, and align on priorities.
• Manage expectations and timelines while balancing analytical depth with business needs.

2. Data Preparation & FeatureEngineering

• Conduct data cleaning, preprocessing, and transformation for both structured and unstructured data to ensure high data quality.
• Perform feature engineering to extract meaningful attributes that enhance model performance.
• Identify relevant datasets and integrate multiple data sources to support analytical work.

3. Analytical Modelling &Experimentation

• Explore data using statistical techniques to uncover trends, patterns, and relationships.
• Build machine learning models for prediction, classification, clustering, or other analytical tasks as required by the use case.
• Evaluate model performance and iterate based on stakeholder feedback and performance metrics.

4. Insights Delivery & Communication

• Translate analytical results into actionable recommendations that stakeholders can apply to drive decisions or business improvements.
• Develop clear, compelling narratives that explain insights and model behaviours in an accessible manner.
• Present analysis with well‑designed visuals, dashboards, and interactive tools that support storytelling and data exploration.

5. Visualisation & User‑Facing Tools

• Design dashboards and interactive visualizations to facilitate self‑service analytics and real‑time data monitoring.
• Apply strong visualization principles to ensure insights are intuitive and impactful.
• Build and deploy customised visual interfaces tightly integrated with underlying data systems.

6. Deployment, Monitoring &Continuous Improvement

• Support model deployment into production environments and collaborate with engineering teams to operationalize models.
• Monitor model performance over time and recommend enhancements as required.
• Contribute to agile project workstreams and participate in ceremonies such as stand‑ups, reviews, and retrospectives.

Required Skills & Competencies

Analytical & Technical Skills

• Ability to convert business challenges into analytical questions and identify appropriate data sources.
• Proficiency in writing data preparation and analysis scripts using tools such as Python, R, SQL, pandas, or equivalent.
• Strong foundation in descriptive statistics, probability, data exploration, and hypothesis testing.
• Skilled in building and validating machine learning models, using modern techniques and frameworks.
• Solid understanding of system design concepts, data structures, and algorithms.

Data Visualization & Storytelling

• Strong command of data visualization principles and tools.
• Capable of developing dashboards and interactive visual tools (e.g., using Tableau, Power BI, Plotly, or custom frameworks).
• Ability to communicate analytical insights through structured storytelling tailored for decision‑makers.

Data Engineering Familiarity

• Understanding of data modelling, data access patterns, and data storage structures such as data lakes, data marts, and data warehouses.
• Familiarity with REST APIs, web protocols, and data extraction via web scraping technologies.
• Comfortable working with big data frameworks (e.g., Spark, Hadoop, Kafka) when required for large-scale processing.

Soft Skills

• Strong communication skills, both written and verbal, with the ability to influence non‑technical audiences.
• Effective stakeholder management and experience working in iterative cycles with feedback loops.
• Organized, self-driven, and able to manage multiple priorities in an agile environment.

Experience Requirements

• Proven experience conducting end‑to‑end data science projects—from scoping and data preparation to modelling and deployment.
• Experience collaborating with diverse teams and managing stakeholders across business and technical functions.
• Prior experience in agile project management or participating in agile delivery teams.

Qualifications

• Degree or equivalent experience in Data Science, Statistics, Computer Science, Engineering, Mathematics, Information Systems, or related fields.
• Experience with production‑grade machine learning workflows is an advantage.

Please send your details in MS Word format to john@oaktree.com.sg:

- Position applying for

- Current remuneration

- Expected remuneration

- Notice period

John Goh Meng Chye

EA License No : 06C4642

EA Reg No : R1102621

We regret that only shortlisted candidates will be notified.

Reporting to the Senior Treasury Manager, this role is part of the Group Treasury team and supports a broad range of treasury functions, including liquidity management, foreign exchange (FX), cash investments, funding, investor relations (IR), and insurance management across the SMRT Group. This role places a stronger emphasis on process automation, data analytics, and digital transformation initiatives within treasury operations.

Responsibilities

You will assist in the following treasury operations & insurance management:

•    Support day-to-day treasury operations, including liquidity management, FX, cash investments, and funding activities.
•    Provide support for critical treasury operations during teammates’ absences to ensure business continuity with timely escalation to the Senior Treasury Manager as needed.
•    Analyse and evaluate current treasury processes and KPIs to identify opportunities for streamlining and automation.
•    Proactively identify opportunities for digital enhancement and process improvements.
•    Enhance and optimize existing spreadsheets, and develop dashboards using Power BI and other tools.
•    Proactively identify opportunities for digital enhancement and process improvements.
•    Drive system improvements within the Treasury Management System (TMS) to improve efficiency and data visibility.
•    Collaborate and assist the Manager, Finance Systems & Digitalisation with treasury automation & AI initiatives and provide adhoc data analysis support and management reporting to meet stakeholder needs.
•    Support audit-related activities, including data preparation, documentation, and coordination.
•    Participate in and support ad hoc treasury and finance projects as required.
•    Provide administrative support, including documentation and filing, where necessary.

Qualifications & Work Experience

•    Bachelor’s degree in Finance, Business, Accountancy, Engineering, Computer Science or Data Science.
•    At least 4 years of relevant experience in Treasury, preferably in listed companies in Singapore.
•    Demonstrated experience in data science, advanced analytics, or business intelligence roles, with a proven track record of applying these skills.
•    A Master's degree or relevant certifications in FinTech, Business Analytics, or Financial Modeling is a strong plus.

Skills

Technical skills include:
•    High level of financial literacy, numeric and analytical capability with good knowledge of financial statements and markets
•    Proficiency in Excel, Power Automate, Power BI, Power Query including advanced functions, VBA and Bloomberg
•    Strong proficiency in SQL for data extraction and manipulation.
•    Working knowledge of Python or R for statistical analysis and scripting automation.
•    Extensive experience with TMS, including system administration, system design, and implementation support.
•    Understanding of Robotic Process Automation (RPA) tools and methodologies for automating treasury tasks.

Generic skills include:
•    Good interpersonal and communication skills
•    Highly driven and ability to thrive in a fast-paced and dynamic environment
•    Good time management skills to prioritize and meet tight deadlines
 

Key Responsibilities & Accountabilities 

• Pre-Sales, Master Planning & Visualization (30%): Develop site master plans, building layouts, and high-quality renderings for client RFP responses; collaborate with Sales to define the "aesthetic and functional" value proposition of a site. 

• Architectural Design & Space Optimization; High Level Civil & Structural Calculations (25%): Lead the design of building envelopes, logistics, and vertical transportation; perform GFA/CFA calculations and neighbourhood impact analyses (GIS) to maximize site utility. Provide simple civil and structural estimations for space planning and cost estimation purposes.  

• Cross-Functional Solution Integration (15%): Follow closely to the technical solution as developed by the Lead Mechanical and Lead Electrical engineers. Proactively ask questions and ensure understanding of the tasks as data center design are very dynamic. Laise with other departments to understand their aesthetic, design and documentation requirements. Partner with the Product Team to integrate MEP and structural modules into the architectural framework; coordinate with Internal Procurement for facade and material selection. 

• Technical Oversight & Delivery Support (5%): Provide ongoing support to the Delivery Team to resolve architectural site issues; review submissions from local Architects of Record (AOR) to ensure alignment with IntraTrak standards. Review vendor samples, finish boards, etc.   

• Compliance & Standards Governance (10%): Develop and maintain company architectural standards, including branding, signage, and "Ways of Working" (WoW) minimalist aesthetics. 

• Business Planning Support (15%): Assist in the production of phasing plans and budgetary estimates for architectural and civil works. 
  

Qualifications & Requirements 

• Education: Bachelor’s or Master’s degree in Architecture. 

• Experience: Minimum of 8 years of professional experience in Architecture, with a preferred focus on industrial or mission-critical facilities. 

• Certifications/Licenses: Licensed Architect by a recognized body (e.g., AIA, RIBA, BOA Singapore, or HKIA) is required. 

• Technical Skills: Expert proficiency in Revit, AutoCAD, BIM, and InDesign; strong capability in architectural rendering and sales-focused sketching. 

• Knowledge: Have a reasonable technical and operational understanding of the needs and requirements of other trades and disciplines. 

  - Discipline 

  * Lead Mechanical 

  * Lead Electrical 

  - Trades 

  * Facilities and Maintenance 

  * Operations and Logistics 

• Languages: Written and oral fluency in English is required; fluency in Chinese is highly desirable for regional project coordination. Additional language proficiencies is also desirable  

• Traits & Behaviors: Minimalist design philosophy ("Less is More"); ability to thrive in a fast-paced, pre-sales driven environment; strong spatial reasoning and logic-driven problem solving. 

Responsibilities:

• Collaborate with data scientists, coders, programmers and other stakeholders
• Creating and managing the AI development process and general infrastructure of a product
• Doing statistical analysis and interpreting findings
• Automating critical tasks and procedures for a data science team
• Creating data transformation infrastructure
• Developing AI models communicating the utility of the AI models they generate to a diverse range of employees inside the organisation, including product managers and management executives
• Exposing machine learning models via APIs with which other apps may interact
• Work on API and integrate systems.

Requirement:

• Master / Bachelor’s degree in computer science, computer engineering, or relevant field.
• A minimum of 4+ years’ experience in a similar role.
• Strong knowledge of Datawarehouse / database / Cloud systems and data mining would be preferred.
• Excellent organizational and analytical abilities.

Interested applicants, Please send your updated resume in Word format to hres@itcspl.com

Please indicate Job Code "Data Engineer - 101" on email subject

We regret that only shortlisted candidates will be notified.

Bacalso John Bryan Bedayo (Reg No. R1106648)

IT Consultancy & Services Pte Ltd

EA No: 92C5311

Please visit www.itcspl.com to learn more information about ITC

• Perform installation, racking, and de-racking of servers, network devices, and IT equipment in data centre environments

• Execute structured cable management, including routing, labelling, and documentation of copper and fibre cabling

• Provide smart hands support for remote engineers, including hardware troubleshooting, patching, and basic configuration

• Monitor and maintain data centre infrastructure to ensure optimal performance and uptime

• Conduct hardware checks, diagnostics, and component replacements (e.g., disks, power supplies)

• Maintain accurate asset inventory, rack layouts, and cable documentation

• Support power-up, connectivity testing, and verification of newly installed equipment

• Adhere to data centre standards, safety procedures, and change management processes

Job Requirements

• Trained / Certified in Information Technology, Telecommunication/Computer Engineering or related fields
• Hands-on experience in Data Center operations, familiarity with facility management, server hardware and network equipment
• Familiar with Windows servers and operating systems
• Familiar with setup and configuration of the server
• Work effectively with minimal direction or supervision for day-to-day tasks and communicate outcomes
• Fast-learning ability, willing to pick up new skills and technologies

· Bachelor's or Technical Degree in Computer Science, IS, or IT is preferred

·  At least 8 years of relevant working experience in Java/J2EE development

· At least 2 years of experience in big data technology development like the Hadoop platform.

·  Good understanding & working knowledge with Agile/Scrum

·   Hands-on experience of end-to-end design and implementation of complex IT systems

· Must have experience in:

o Java/J2EE, Spring, Spring Boot, RESTFUL Webservices.

o Strong knowledge in Core Java (Java 8+).

o   Experience in Kafka messaging system.

o   Big Data framework like Storm, Hadoop, Hdfs, Hbase, Ignite.

o   SQL & No-SQL database like PostgreSQL, MySQL, Hbase, MongoDB.

o   Knowledge of Flink,Redis, s3, MongoDB is an advantage.

o   Tools – Maven, GIT CICD, Cucumber, Kibana.

o   Knowledge of containerization technologies like Docker & Kubernetes.

o   Solution design using proven patterns, awareness of anti-patterns, performance benchmarking.

o   TDD driven development mindset with strong skills in Junit, Mockito and other Unit testing frameworks.

o   Knowledge on DevOps Practices.

Situated in the heart of Singapore's Central Business District, Rakuten Asia Pte. Ltd. is Rakuten's Asia Regional headquarters. Established in August 2012 as part of Rakuten's global expansion strategy, Rakuten Asia comprises various businesses that provide essential value-added services to Rakuten's global ecosystem. Through advertisement product development, product strategy, and data management, among others, Rakuten Asia is strengthening Rakuten Group's core competencies to take the lead in an increasingly digitalized world.

Department Overview

The Data Lake house Section provides the foundation for Rakuten Digital Twin strategy enabling seamless, real-time understanding of customers across services. 
We manage one of the world's largest first-party datasets, developing and operating products like Customer DNA (CDNA) and Deep CDNA, which unify data across business units. 

As a Senior Data Engineer, you will play a key role in the architecture, development, and optimization of Rakuten's core data platform. 
You will lead engineering efforts to design scalable, reliable, and secure data pipelines and collaborate closely with global teams in Japan and India.

Responsibilities:

- Lead the design, development, and operation of scalable and reliable data pipelines supporting Customer DNA and related data products.
   
- Drive best practices in data engineering, including performance tuning, automation, and observability.
   
- Collaborate with product managers, data scientists, and analytics engineers to deliver end-to-end solutions.
   
- Oversee data ingestion, transformation, and quality control processes to ensure trusted data availability.
   
- Design and optimize data architectures leveraging Google Cloud Platform(GCP) services (e.g., Big Query, Dataflow, Pub/Sub, Cloud Storage).
   
- Mentor team members, promote engineering excellence, and contribute to a culture of learning and collaboration.
   
- Ensure compliance with data governance, security, and privacy standards across systems.
   
Qualifications:

- 5+ years of experience building and operating big data platforms for analytics or business operations.
   
- 2+ years of experience managing large-scale, real-time or unstructured datasystems in distributed environments.
   
- Proficiency in Python programming and related libraries (e.g., Pandas, NumPy, PySpark, Pydantic).
   
- Strong hands-on experience with Google Cloud Platform (GCP), including Big Query, Cloud Storage, Cloud Functions, Dataflow, Pub/Sub, and IAM.
   
- Experience with Infrastructure-as-Code (IaC), CI/CD pipelines, and cloud resource management.
   
- Experience with **containerization (Docker)** and **orchestration (Kubernetes)** in cloud-native environments.
   
- Strong understanding of **SQL and NoSQL** data stores, with experience inlarge-scale distributed systems.
   
- Proven ability to identify and solve complex data engineering challenges, ensuring system reliability and scalability.
   
- Excellent communication and leadership skills, with experience guidingcross-functional or remote teams.
   
- Business-level proficiency in English.

Good to have:

- Experience designing Python-based data engineering frameworks ensuring scalability, performance, and maintainability.
   
- Experience developing and maintaining reusable Python packages and libraries for data pipelines.
   
- Familiarity with API-based data integration and micro service architectures.
   
- Experience implementing cloud-native data solutions to optimize cost and performance.
   
- Proven ability to balance hands-on technical contribution with strategic leadership.
   
- Ability to mentor engineers and promote best practices in software craftsmanship.
   
Rakuten is an equal opportunities employer and welcomes applications regardless of sex, marital status, ethnic origin, sexual orientation, religious belief, or age.
   

Data engineer (Azure) – Synapse and Pyspark, Python, Datawarehouse and Azure Data Explorer, Azure Devops

Job Scope

• Design, review and development of Pyspark scripts. Testing, troubleshooting of data pipelines, orchestration.

• Designing and developing reports and dashboards in PowerBI,setting up access control with row level security DAX query experience.

• Establishing connections to source data systems such as on-prem databases, IOT devices, APIs.

• Managing the collected data in appropriate storage/data-base solutions e.g. file systems, SQL servers, Big Data platforms such as Hadoop, HANA, etc. as required by the specific project requirements.

• Design, development of relevant data pipelines using pyspark, copy data activities for batch ingestion.

• Performing data integration e.g. using database table joins, or other mechanisms at an appropriate level as required by the analysis requirements of the project.

• Deployment of pipeline artifacts from one environment to the other using Azure Devops.

Skills & Experience

• Bachelor’s Degree in Computer Science or Engineering with 2 years of experience in Azure Data engineering, Python, Pyspark or Big Data development.

• Sound Knowledge of Azure Synapse analytics for pipelines, orchestration, set up.

• 1-2 experience in Visualization design and development with Power BI. Knowledge on row-level security, access control.

• Sound experience in SQL, Datawarehouse, data marts, data ingestion with Pyspark and Python.

• Expertise in developing and maintaining ETL processing pipelines in cloud-based platforms such as AWS, Azure, etc. (Azure Synapse or data factory preferred)

• Team player with good interpersonal, communication, and problem-solving skills.

• Preferred to have Devops expertise.

Working hours :

8:30am to 6pm (Monday to Friday) onsite, no hybrid option.

JOB OVERVIEW:

The Data Scientist (AI Engineer) serves as the critical technical bridge between high-level business strategy and scalable AI production. This role is dedicated to accelerating demand creation by translating complex client challenges into feasible, high-impact AI/ML prototypes and Proof of Concepts (PoCs).

By balancing technical rigor with strategic communication, the incumbent assesses data maturity, designs agile solutions using both traditional and Generative AI, and ensures that all innovations are architecturally sound and ready for production handover. Ultimately, this role drives organizational growth by de-risking AI investments and demonstrating the tangible technical value of emerging technologies.

Core Focus Areas

• Technical Translation: Converting business "pain points" into rigorous technical requirements and feasible AI architectures.

• Rapid Innovation: Designing and iterating on interactive prototypes that showcase the "art of the possible" while adhering to vetted Technology Readiness Level (TRL) standards.

• Strategic Advisory: Evaluating client data infrastructure and providing technical roadmaps to bridge the gap between current capabilities and AI readiness.

• Seamless Integration: Partnering with Production and Enablement leads to ensure prototypes are scalable, documented, and transitioned without technical debt.

• Technology Scouting: Researching and applying cutting-edge tools from the internal Tech Radar to solve real-world client problems.

DUTIES AND RESPONSIBILITIES:

Technical Requirements Elicitation & Analysis:

• Collaborate with AI/ML Business Analysts and clients to deeply understand their existing technical landscape, data infrastructure, and specific operational challenges.

• Translate high-level business needs into technical requirements for AI/ML solutions, identifying necessary data inputs, system integrations, and potential technical constraints.

• Assess the technical feasibility of proposed AI/ML use cases, ensuring alignment with the organization’s vetted tech stack and TRL standards, given the client's current data capabilities and technology stack.

AI/ML Solution Conceptualization & Prototyping:

• Design and develop rapid, interactive prototypes or proof-of-concepts (PoCs) using appropriate AI/ML techniques (both traditional ML and cutting-edge AI like Generative AI).

• Demonstrate the functionality and potential business impact of AI/ML solutions through compelling technical demonstrations and visualizations.

• Iterate rapidly on prototypes based on client feedback, showcasing agility and responsiveness.

Technical Communication & Value Articulation:

• Simplify complex AI/ML concepts, algorithms, and technical architectures into understandable terms for non-technical business stakeholders.

• Clearly articulate the technical value proposition of proposed solutions, explaining how the AI/ML will work and why it is the right approach for the client's problem.

• Present technical findings, prototype results, and architectural considerations to clients and internal teams.

Organizational Data Capability Assessment (Technical):

• Conduct technical assessments of client data maturity, including data quality, accessibility, governance, and existing infrastructure.

• Advise clients on technical prerequisites and potential roadmaps for enhancing their data capabilities to support AI/ML initiatives.

Emerging Technology Research & Application:

• Actively research and evaluate new AI/ML technologies, tools, and frameworks (e.g., specific algorithms, cloud AI services, MLOps tools) relevant to demand creation and prototyping.

• Support the AI Solution Enablement Lead by exploring specific application-layer use cases for technologies already on the internal Tech Radar. Provide field feedback on the performance of vetted tools in client prototypes

• Identify how emerging technical capabilities can be applied to solve client problems and create new service offerings.

• Contribute technical insights to market exploration and strategic planning.

Cross-Functional Collaboration & Handover:

• Work in close partnership with the Data Science / AI Production Team from the earliest stages to ensure that prototyped solutions are designed with production scalability and maintainability in mind.

• Collaborate primarily with the AI Solution Enablement Lead to transition validated TRL 3 prototypes into the hardening phase. Ensure all initial technical elicitation is documented according to Enablement Quality Gate standards to prevent technical debt during handover

• Facilitate smooth technical handovers of validated concepts, prototypes, and initial architectural considerations to the production team, minimizing rework and ensuring continuity.

• Provide technical context and support during the transition phase to ensure the production team has a comprehensive understanding of the solution's intent and design.

• Problem Solving & Innovation:

• Apply strong analytical and problem-solving skills to technical challenges encountered during the demand creation and prototyping phases.

• Propose innovative technical approaches to address client needs and differentiate our solutions.

QUALIFICATIONS AND EXPERIENCES:

• Minimum Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field. Master's or Ph.D. preferred.

• At least 5+ years of progressive experience in AI/ML solution design, data science, or AI engineering, with a significant portion in a client-facing or consulting capacity.

• Proven experience in designing, developing, and deploying functional AI/ML prototypes that demonstrate business value and are engineered for production.

• Expert proficiency in Python and extensive experience with core AI/ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).

• Demonstrated hands-on experience with Generative AI and Large Language Models (LLMs), including frameworks like LangChain, for designing and prototyping novel applications.

• Strong practical experience with Google Cloud Platform (GCP) services relevant to AI/ML (e.g., Vertex AI, BigQuery, Cloud Storage, Dataflow, Cloud Functions).

• Proficiency with Docker for containerization and a foundational understanding of container orchestration (e.g., Kubernetes concepts for GKE).

• Solid understanding of MLOps principles for reproducibility, experiment tracking (e.g., MLflow, Vertex AI ML Metadata/Model Registry), and model versioning.

• High-level expertise in data governance frameworks, data quality assessment (including tools/libraries like Great Expectations), metadata management (e.g., Google Cloud Data Catalog), and data architecture principles.

• Strong expertise in distributed computing and parallel processing techniques is vital for handling large-scale AI workloads. 

• A proven track record of innovations and executions in deep learning, demonstrated through shipping products or first-author publications at leading AI conferences, is a strong differentiator.

• Experience in the container shipping industry or related logistics/supply chain domains is highly advantageous.

• Excellent presentation, documentation, and stakeholder management skills.

• Experience Requirements:
• Must have QA/Testing Experience.
• Data Testing Experience specifically in Big Data, Hadoop, or Cloud Data Warehouse environments.
• Databricks Experience of testing pipelines within a Databricks environment.
• Automation Focus: Proven track record of moving from manual SQL checks to automated Python-based testing frameworks.
• Mandatory: Databricks Certified Data Engineer Associate (at minimum).
• Preferred: ISTQB Foundation or Advanced Level (Test Automation Engineer).
• Core Technical Skills:
• Data Validation & Frameworks
• Great Expectations / Pandera: Proficiency in using Python-based libraries to define data "contracts" and automated validation suites.
• DLT Expectations: Deep understanding of Delta Live Tables (DLT) expectations (Fail, Drop, Quarantining bad records).
• Advanced SQL: Expert-level SQL for complex data reconciliation, identifying duplicates, and null-value analysis across billions of records.
• Python for QA (PySpark):
• Pytest-Spark: Experience using pytest to write unit tests for PySpark transformations and logic.
• Notebook Testing: Ability to write automated test notebooks that validate Medallion Architecture transitions (Bronze to Silver, Silver to Gold).
• Data Reconciliation: Building Python scripts to perform "source-to-target" counts and checksums across distributed file systems.
• Performance & Integration Testing:
• Scalability Testing: Ability to validate that data pipelines meet performance SLAs when data volume spikes.
• End-to-End Orchestration Testing: Testing the reliability of Databricks Workflows and handling of job failures/retries.
• Schema Evolution: Testing how pipelines handle upstream schema changes without breaking downstream Gold tables.
• Governance & Security Testing:
• Unity Catalog Validation: Testing Row-Level Security (RLS) and Column-Level Masking to ensure unauthorized users cannot see sensitive data.
• Data Lineage: Validating that data lineage in Unity Catalog correctly reflects the movement of data across the Lakehouse.

CLC is an investment management firm with offices in Singapore and Hong Kong. Applicants are invited to apply for a data analyst position based in Singapore. The position provides the opportunity for global exposure within a supportive and performance-driven team environment.

Key responsibilities:

• Work with data sets across various data sources and conduct data analysis to identify insights
• Build and maintain models, databases and data analytics tools
• Conduct analysis and research related to companies, industries, economies and financial markets
• Ensure quality of data and enhancements
• Produce internal reports and presentations

Key requirements:

• Strong academic background with degree in finance, economics, mathematics, statistics, computer science, engineering or other quantitative discipline
• Experience in data analytics, quantitative analysis or statistical computing well-regarded
• Relevant industry qualification well-regarded

• Excellent analytical, modelling and numerical skills
• Driven and passionate about data analytics and research
• Reliable, accurate and detail-oriented with strong work ethic
• High professional standards and integrity
• Good written and verbal communication skills

Competitive remuneration commensurate with experience. Qualified applicants should submit their resume and a cover letter.

Only shortlisted candidates will be notified. All data will be used for recruitment purposes only.

JOB SUMMARY:

We're looking for a Data Engineer with strong software engineering fundamentals to build and scale our data infrastructure. You'll design high-performance systems that handle massive data volumes and high-concurrency workloads, optimize query engines for speed and reliability, and architect robust data pipelines. This role is for engineers who love solving distributed systems challenges and building production-grade infrastructure that processes terabytes of data daily.

This role is open to fresh graduates and early-career professionals (1–2 years of experience) who are eager to grow their skills in building scalable data systems.

Whether you’re just starting out or already have some experience, this is a great opportunity to develop your data engineering skills and contribute to impactful, data-driven decision-making.

KEY RESPONSIBILITIES:

• Design, develop and maintain data infrastructures, such as computation, query engine and services which can support massive data and high concurrency requests
• Clean, transform, and optimize raw data for storage and analysis
• Work with structured and unstructured data from various sources (databases, APIs, files, etc.)
• Plan data warehouse structure to ensure data quality, accuracy, consistency, and availability
• Optimise for the performance of the data pipeline and service to downstream
• Collaborate with business analysts, data scientists, and backend teams to provide appropriate tools
• Document data models, processes, and technical decisions

What You'll Learn:

• Real-world data engineering with modern tools like Apache Spark/Flink, Kafka, Airflow
• Working with SQL/NoSQL databases, data lakes, and cloud platforms (AWS, GCP, Azure)
• Building batch and streaming data pipelines
• Data modeling, warehousing
• Orchestration and monitoring of data workflows
• Developing high availability services
• Best practices in data governance, privacy, and security
• Collaboration in agile, cross-functional teams with product, engineering, and analytics

JOB REQUIREMENTS: