Total 999 data entry job vacancies in Information Technology category

• Coordinate research activities and ensure projects remain on track across different stages.

• Manage vendors and review research materials, including questionnaires and code frames.
• Test surveys and review data outputs to ensure accuracy, consistency, and quality.
• Consolidate and prepare datasets from multiple sources, ensuring data reliability.
• Create clear and impactful data visualisations using Excel (e.g. charts, pivot tables, dashboards) to support data‑driven decision‑making.
• Support the enhancement of data dashboards through planning and testing.

We’re Looking for Someone Who:

• Has strong organisational skills and thrives in a fast‑paced, deadline‑driven environment.
• Pays close attention to detail and takes pride in ensuring data accuracy.
• Is highly proficient in Excel and comfortable working with large datasets.
• Communicates clearly and effectively with internal and external stakeholders, both written and verbally.

Interested candidate please click"APPLY" to begin your job search journey and submit your CV directly through the official PERSOL job application platform - GO Mobile.

We regret to inform that only shortlisted candidates will be notified.

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PERSOL Singapore Pte Ltd • RCB No.200007268E • EA License No. 01C4394• Reg No: R1440784 (Foo May Cheng) 

- 1 year contract (convertible based on performance)

- Up to $6,500

- Mon-Fri: 9am-6pm

- Telok Ayer

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. 

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. 

Period:

•  1 year contract (convertible based on performance)

Working Hours:

• Mon-Fri, 9am-6pm 

Salary: 

• Up to $6,500 

Location: 

• Telok Ayer

Requirements:

• 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. 

Interested applicants, kindly email your detailed resume (MS Word format is preferred):
zikry@successhrc.com.sg  (Registration no: R2198989)

Please ensure that applications sent through email are no bigger than 1Mb.

We thank all applicants for your interest but regret to inform that only shortlisted candidates would be notified.

Success Resource Centre Pte Ltd (EA License Number: 04C3201)

160 Robinson Road, #13-07/08/09 SBF Center, Singapore 06891

T: 6337 3183 | W: www.successhrc.com.sg 

Product area

Whether it is paying online with Autofill, using tap and pay in stores, or using the Google Pay app, the Payments team at Google is focused on making payments simple, seamless, and secure. In addition to consumer payment technologies, the Payments team also powers the money movement between Google and its consumers and businesses.

Job description

As a Technical Solutions Consultant, you will be responsible for the technical relationship of our largest advertising clients and/or product partners. You will lead cross-functional teams in Engineering, Sales and Product Management to leverage emerging technologies for our external clients/partners. From concept design and testing to data analysis and support, you will oversee the technical execution and business operations of Google's online advertising platforms and/or product partnerships.

You will be able to balance business and partner needs with technical constraints, develop innovative, cutting edge solutions and act as a partner and consultant to those you are working with. You will also be able to build tools and automate products, oversee the technical execution and business operations of Google's partnerships, as well as develop product strategy and prioritize projects and resources.

Qualifications

Job responsibilities

• Design and build agentic analytics systems leveraging Large Language Models (LLMs) and AI to automate insight generation and workflows. Translate product needs into production-grade data systems (models, pipelines, and services).
• Lead the design and coordination of analytics infrastructure powering product analytics across Payments.
• Build AI-native analytics workflows, embedding intelligence directly into data and decision systems.
• Partner with Product, Data Science, and Engineering to shape problem definitions and deliver the solutions.
• Drive data quality, reliability, and governance standards across critical analytics systems. Build internal tools and frameworks to enable self-serve analytics and faster iteration for product teams.

Minimum qualifications

• Bachelor's degree or equivalent practical experience.
• 5 years of experience coding in Python and SQL.
• 5 years of experience designing and deploying data pipelines, including managing data schemas and processing data volume workflows.
• 5 years of experience working with machine learning operations and data architecture design.

Preferred qualifications

• 5 years of experience designing enterprise-scale data platforms and analytics infrastructure.
• 5 years of experience with AI/LLM-based systems, agentic workflows, or intelligent data applications.
• 5 years of experience with using Gemini Command-line Interface, Cider Agents, LLM Extensions, Agent Development Kit (ADK) Agents etc.
• 3 years of experience partnering with stakeholders (e.g., users, partners, customer), and managing stakeholders or customers.
• Experience with Machine Learning for production workflows.
• Ability to operate across ambiguity and influence cross-functional technical decisions.

Responsibilities

• Explore new external data sources to understand availability and quality and make them easily accessible to different stakeholders for different use cases
• Develop solutions that enable investment professionals and data science teams to efficiently extract insights from data. This includes owning the ingestion and transformation
• Work with multiple stakeholders [internal end user, external technical consultants] to drive the development of firm’s enterprise data catalogue system
• Partner with the investment professionals, quantitative researchers, and data scientists to design, develop and deploy solutions that answer fundamental questions about companies, sectors, countries and financial markets
• Build tools and automation capabilities for data pipelines that improve the efficiency, quality and resiliency of our data platform
• Drive the evolution of our data strategy by challenging the status quo and identifying opportunities to enhance our platform

Requirements

• Minimum 7+ years experience in data engineering with a passion for data and software development
• Proficient in Data Warehousing, Machine Learning, and CI/CD workflows like Jenkins
• Skilled in managing multiple Docker deployments and deploying microservices for backend and ML model hosting
• Experience with cloud platforms: AWS, Azure Cloud, Kubernetes, and preferably Snowflake or similar data warehouse tech
• Programming proficiency in Python and SQL with knowledge of data caching and cloud data reuse strategies
• Background in quantitative hedge funds or technology companies preferred; strong communication skills required
• Bachelor’s or Master’s in Computer Science or equivalent experience
• Bonus skills include experience with Generative AI and rapid prototyping frameworks like Streamlit

Job Description

As a Data Platform Engineer, you will operate large-scale big data platforms across hybrid (on-premises and cloud) environments, enabling reliable analytics and data-driven use cases. You will work closely with data engineers, data scientists, infrastructure, security, and business stakeholders to ensure data quality, platform stability, and operational excellence.This role focuses on building, running, and optimizing production-grade data platforms and pipelines, with strong ownership of infrastructure, automation, reliability, and operations. 

Key Responsibilities

• Design, implement, and manage scalable data platform infrastructure and pipelines across on-premises and cloud environments.
• Own the end-to-end platform lifecycle, including architecture design, deployment, operations, performance optimization, and reliability engineering.
• Maintain and support data platform clusters and nodes (compute, storage, networking), ensuring high availability and optimal performance.
• Provision, configure, and manage cloud-based data services such as AWS S3, Redshift etc.
• Monitor platform health, performance, and capacity; implement observability, alerting, and operational runbooks to ensure system reliability.
• Support and optimize ETL/ELT pipelines, ensuring reliable data ingestion, transformation, and delivery.
• Operate and maintain data storage platforms (on-premises and cloud), ensuring durability, scalability, and cost efficiency.
• Implement and enforce security best practices, including IAM, VPC configurations, encryption, backup strategies, and disaster recovery.
• Ensure compliance with data governance and regulatory requirements (e.g., PDPA) in collaboration with infrastructure and security teams.
• Collaborate with data engineers, data scientists, and cross-functional stakeholders to align platform capabilities with business and analytical needs.
• Develop and maintain technical documentation, including system architecture, data flows, configurations, and operational procedures.

Requirements

• Bachelor’s degree in computer science, Information Technology, Engineering, or a related field, or equivalent practical experience.
• 2–5+ years of experience in System Engineering, Infrastructure Engineering, DevOps, or Data Engineering.
• Strong hands-on experience managing Linux-based systems, including configuration, patching, performance tuning, and troubleshooting.
• Experience supporting on-premises or cloud infrastructure (e.g., AWS), including compute, storage, and networking components.
• Familiarity with operating distributed systems or data platforms (e.g., Hadoop, Spark, Airflow), focusing on deployment, monitoring, and troubleshooting rather than development.
• Solid understanding of networking fundamentals, including TCP/IP, DNS, routing, firewalls, load balancing, and VPC design.
• Knowledge of system and platform security practices, including IAM, access control, encryption, patch management, and basic compliance requirements.
• Experience with monitoring and observability tools (e.g., Prometheus, Grafana, ELK, CloudWatch) for system health, alerting, and incident response.
• Strong troubleshooting and problem-solving skills in production environments, with the ability to diagnose issues across infrastructure, network, and platform layers, and collaborate effectively with cross-functional teams to ensure platform reliability and operations

Job Title: Data Engineer
Duration: 24 months
Location: 1 Marina Boulevard
Salary: 5000-6000
Eligibility: Only Singaporeans

As a Data Engineer, you will be a key member of the Data Management and Operations team, ensuring a robust data infrastructure, upholding data quality and data governance rules and efficient functioning of data pipelines. You will work closely with stakeholders across the division and vendors to ingest data and translate data needs into the relevant tables. The Data Engineer will also work closely with AI Engineers and Machine Learning Engineers to deploy data science models into production.

What you will be working on

You will be involved in a range of tasks including the following:

• Infrastructure Architecture & Cloud Management: Design and manage robust, scalable data infrastructure utilising AWS and other cloud platforms based on project requirements. Proactively explore and evaluate innovative data engineering tools to enhance infrastructure capabilities. Continuously recommend and implement improvements to data infrastructure based on emerging technologies.
• Data Pipeline Development & Implementation: Design and implement efficient data models and pipelines for ingestion, processing, and distribution of largescale datasets. Ensure high data quality and availability across all data processing workflows. Align data flows across various systems with consistent schema and governed access models. Collaborate with external vendors to enable secure data exchange through APIs, including the development of API Swagger specifications required for vendors to build and integrate their API endpoints.
• Data Quality & Standards Management: Lead comprehensive data quality initiatives establishing standards for accuracy and reliability across all systems. Diagnose and resolve data pipeline issues while contributing to incident response and post-mortem reviews. Maintain rigorous quality control processes throughout the data lifecycle.
• AI & Machine Learning Collaboration: Collaborate closely with AI engineers to provide optimised data solutions for machine learning projects. Emphasise seamless data flow and accessibility for AI model development and deployment. Ensure data infrastructure supports advanced analytics and machine learning requirements.
• Documentation & Knowledge Management: Develop and maintain comprehensive documentation on data architecture, procedures, and management practices. Ensure clarity and consistency of documentation across all teams and projects. Create accessible resources that support effective data management practices.
• Automation & Integration Solutions: Leverage innovative tools and architectures to automate common, repeatable, and error-prone data preparation tasks. Minimise manual processes whilst improving overall productivity and efficiency.

Implement automated data integration solutions that reduce operational overhead.

• Governance & Stakeholder Collaboration: Work closely with data governance teams to vet and promote high-quality content for governed reuse. Engage proactively with cross-functional teams and business stakeholders to refine requirements and co-design solutions. Support creation and maintenance of curated data catalogues for organisational use.
• Training & Continuous Improvement: Facilitate knowledge sharing and technical training sessions on data management best practices and tools. Enhance data competency across staff through structured learning programmes. Establish regular feedback loops with data consumers to refine and optimise pipelines for seamless production deployment

Requirements

• Proficiency in data engineering practices (including versioning, release management, deployment of datasets, agile & related software tools) and building scalable data pipelines.
• Proficiency in Python and SQL; experience in AI/ML model development and deployment a plus.
• Experience in working with large and multiple datasets and data warehouses.
• Independent contributor with ability to collaborate and work effectively within the team.
• Strong analytical, conceptualization and problem-solving skills.
• Excellent written and verbal communication skills

Apply Now via MyCareersFuture
Only qualified candidates are contacted

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In Ref: SS – JO-28275-B
BGC Group Pte Ltd (Outsourcing)
EA 05C3053

Your Mission

This position is responsible for applying data-driven problem-solving, data science, and optimization techniques to resolve real-life manufacturing problems and deploy the ideated solutions in a production environment.

What To Expect

[Data Analysis & Insights Generation]

• Analyze large, complex datasets to identify trends, patterns, and derive key underlying drivers of performance and how to further optimize them
• Identify opportunities for automation, process optimization, and innovation using data-driven approaches

[Data Science Solutions]

• Design, build, and deploy machine learning models and algorithms to solve business challenges and improve operational efficiency
• Collaborate with other team members to integrate AI/ML solutions into use case applications
• Train engineers and technicians to leverage the insights for improving their day-to-day performance

[Data Visualization & Reporting]

• Create interactive dashboards, reports, and visualizations to communicate insights to non-technical stakeholders.
• Use tools like Power BI, or Python libraries to present findings effectively

[Collaboration & Stakeholder Engagement]

• Participate as a data scientist in multi-disciplinary project teams aiming to improve the performance of our production environment in specific areas such as logistics and supply chain, assembly, maintenance, and quality
• Act as a subject matter expert on data science, providing guidance and support to project teams
• Recommend and implement new tools, techniques, and methodologies to enhance project outcomes

What You'll Bring

• Bachelor's or Master's degree in Industrial Engineering, Computer Science, Statistics, Mathematics, or equivalent practical experience.
• 5 years of project execution experience as a data scientist, resolving industrial problems across the value chain, including manufacturing, and ensuring the adoption of data science solutions.
• Preferred experience in the manufacturing and/or automotive industry.
• Strongly preferred experience in implementing data science and/or AI projects in real-life industrial environments (large POCs or at-scale deployments).
• Critical end-to-end problem-solving skills, including rapid and rigorous issue analysis, idea development and implementation, and feasibility demonstration and deployment at scale.
• Experience with data visualization tools like Tableau, Power BI
• Experience in applying Regression/Classification/Clustering models, Large scale data analysis, Time series analysis, Forecasting models, or Kernel-based methods.
• Experience in algorithm development using Mathematical programming (Linear programming, Mixed-integer programming), (Meta)Heuristic algorithms, Stochastic Process, or Combinatorial Optimization.
• Expertise in processing large-scale datasets in distributed data frameworks (Hadoop, Spark, or Hive).
• Expertise in data mining frameworks such as PyTorch, TensorFlow, Scikit-learn, or MLlib.
• Expertise in applying machine learning, Generative AI, LLMs is a plus.

Role: Data Scientist (Machine Learning) – AI Fintech

Location: Singapore

We are partnering with a leading AI Fintech seeking a Data Scientist with expertise in banking models, fraud analytics, and machine learning. You will design predictive models, detect fraud patterns, and collaborate with Risk, Product, and Engineering teams to deliver data-driven solutions.

Responsibilities:

• Build and deploy machine learning models for credit risk, payments, and fraud detection.
• Analyze transactional data to identify fraud patterns and emerging risks.
• Perform feature engineering, model training, validation, and monitoring.
• Collaborate with cross-functional teams to implement models into production systems.
• Ensure all models meet regulatory and compliance standards.

Requirements:

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or related field.
• 3+ years of experience in data science, ideally with a focus on banking or fraud analytics.
• Strong skills in Python, SQL, and machine learning tools; experience with GenAI and LLMs is a plus.
• Solid understanding of banking products, fraud schemes, and risk modeling techniques.
• Hands-on experience with supervised and unsupervised machine learning, applied statistics, and AI methods.

UEN: 202329526G

EA Licence: 23S2061