Total 343 data entry job vacancies

• Up till $3,500 Basic + Bonus

• 5 days work week

• No experience welcomed
  

Our Client, a leading supplier of sensors, measuring systems, laser markers, microscopes, and machine vision systems worldwide, is inviting qualified candidates to fill the position as Data & Marketing Officer.

RESPONSIBILITIES:

• A chance to make a significant impact with your data analysis skills from the very beginning, increasing company performance

• Creating data to serve as a foundation for sales analysis and market development using software from Microsoft, SAP, and Keyence’s own software

• Not just updating existing data, but using it to guide sales direction and actions

• In the Marketing department, tasks include promotional actions, lead generation, and contributing to sales strategies. This position is suitable for those eager to gain a wide range of experiences in all B2B marketing initiatives
  

REQUIREMENTS:

• Degree in Marketing, Business and Engineering

• At least 1 year of relevant experience

• No experience are welcomed to apply

• No data analysis experience is fine as long willing to learn

• Must possess proficiencies in Excel and PBI/Tableau.

• Power Automate will be a plus

Purpose of the role – mission: The Data Scientist role is responsible for designing and managing universe studies, setting up the extrapolation for Retail Panels, documenting methodologies, ensuring quality assurance, and monitoring key performance indicators (KPIs) related to Panel Health. This role requires a strong background in data analysis, statistical modeling, and quality control processes as well as strong production system processes for PoS. This role supports the China market.  

Key activities:  

• Methodology and Universe Studies: Design and manage universe studies, including project management.  

• Document methodologies for transparency and provide clear explanations of existing methods.  

• Review results of universe studies and insert changes in the POS Manager.  

• Conduct impact analysis of universe studies.  

• Design and simulate extrapolation setups for new panels and sign off on the designs.  

• Quality Assurance: Optimize samples by analyzing shop contributions, detecting MDQC (Market Data Quality Check) outliers, meeting target sample requirements, and addressing issues like empty shops and slow sales.  

• Detect and address systematic methodological issues and prepare cleanup impacts for critical findings (end-to-end).  

• Modeling and Extrapolation: Model missing retailers completely outside the system and maintain complex models.  

• Support the setup of modeling missing retailers in the system and hand over to Stat Ops for ongoing maintenance and execution.  

• Implement new country-channel setups, including reviewing channel definitions, designing universes and samples, and providing setup instructions.  

• Quality Monitoring: Resolve Level 2 methodology clarification issues.  

• Monitor KPIs and conduct root cause analysis (RCA) for panel health, focusing on samples, missing retailers, universes, copy shops, and MDQC related to samples and extrapolation.  

• Define and specify improvement points for panel health.  

The Data Operations Analyst  is responsible of reformatting the retailer and vendor input files, validations, outlier checks and sending the deliverables to the next workstream in a correct and timely manner.  

• Be the contact for questionable sales and stores trend, leading the resolution in a client-focused and timely manner in cooperation with Input Validation, Retailer Vertical/ Retailer Engagement team and other downstream functions. To be able to identify, investigate and coordinate the resolution of data, process, or product related queries 
• Plan and execute complex daily personal workload and support to meet departmental and company schedules 
• Analyze and identify gaps and areas for improvement in reformatting and validations environment (CIP – Consumer Information platform) and drive convergence of processes and tools 

• Providing accurate and timely feedback to respective country teams, driving SOP and use of best-demonstrated practices 
• Working across relevant Operations/Enablement teams to ensure delivery to client expectations and satisfaction 
• Operate in a virtual/multi-cultural environment, liaising with stakeholders and colleagues

As a Data Scientist at Micron, you will employ techniques and theories drawn from areas of mathematics, statistics, semiconductor physics, materials science, and information technology to uncover patterns in data from which predictive models, actionable insights, and solutions can be developed.

You will interact with experienced Data Scientists, Data Engineers, Business Areas Engineers, and UX teams to identify questions and issues for data analysis projects and improvement of existing tools. In this position, you will help develop software programs, algorithms and/or automated processes to cleanse, integrate, and evaluate large datasets from multiple disparate sources. There will be significant opportunities to perform exploratory and new solution development activities.

In This Role You Will Have The Following Responsibilities

• Strong desire to grow a career as a Data Scientist in highly automated industrial manufacturing doing analysis and machine learning on terabytes and petabytes of diverse datasets and to deliver meaningful end-to-end solutions for users.

• Experience in the areas: computer vision, statistical modeling, deep learning, generative AI, feature extraction and analysis, supervised/unsupervised/semi-supervised learning. Exposure to the semiconductor industry is a plus but not a requirement.

• Design and deploy computer vision models for object detection, image segmentation and anomaly detection.

• Stay abreast of the latest research and advancements in machine learning and computer vision and apply them to real-world problems.

• Ability to extract data from different databases via SQL and other query languages and applying data cleansing, outlier identification, and missing data techniques.

• Coordinate production deployment activities, including cross-functional communications, approval and execution of changes in live environments.

• Strong verbal and written communication skills.

Required Qualifications and Technical Experience

• Mater/PHD Degree in Operation Research, Industry Engineering, Statistics with a strong academic foundation in linear programming and optimization

• Minimum 3 years of experience in operation research/optimization, preferably has working experience in the semiconductor industry and have applied optimization techniques in their daily work

• Strong foundation in Linear programming optimization modeling, advanced analytics, and  machine learning techniques.

• Ability to extract data from multiple databases using SQL and other query languages, including experience with data cleansing, outlier detection, and handling missing data.

• Strong proficiency in Python and linear programming, SQL, C#

• Proficiency in data visualization tools such as Tableau, Power BI, and related techniques.

• Proficiency in web development technologies, including Angular, FastAPI, and Docker.

• Manage Lockout/Tagout (LOTO) procedures and ensure compliance across all contractors.
• Act as the primary liaison between construction, commissioning, and operations teams regarding energy safety.
• Review and approve energy isolation plans and permits.
• Conduct field inspections and audits to ensure adherence to safety protocols.
• Stop unsafe work practices and enforce OSHA and NFPA 70E standards.
• Monitor and report on energy usage and performance metrics.
• Validate mechanical and electrical system performance during commissioning.
• Assist in implementing energy-saving strategies and sustainability best practices.
• Maintain detailed records of energy control activities and compliance metrics.
• Train and mentor teams on electrical safety and energy isolation procedures.

Responsibilities

• Develop and maintain ETL/ELT pipelines using Python, PySpark, and SQL.
• Build and support batch and real-time data processing solutions.
• Integrate data from multiple sources and ensure data quality and reliability.
• Monitor, troubleshoot, and optimize data pipelines and workflows.
• Collaborate with business, analytics, and technology teams to deliver data-driven solutions.
• Support cloud-based data platforms and data warehousing initiatives.
• Develop automation and monitoring solutions to improve operational efficiency.
• Any other ad-hoc duties as assigned by supervisor.

Requirements

• Bachelor's Degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline.
• 3+ years of Data Engineering or ETL development experience.
• Strong proficiency in Python, PySpark, and SQL.
• Experience with Hadoop, Spark, Kafka, or similar big data technologies.
• Knowledge of data warehousing and data modeling concepts.
• Experience with cloud platforms such as Azure Data Factory, Azure Synapse, Databricks, or Azure Data Lake.
• Strong problem-solving and stakeholder management skills.

Technical Skills

• Experience with Apache Flink or real-time streaming technologies.
• Exposure to Power BI, Tableau, Qlik, or other BI tools.
• Familiarity with AI/LLM frameworks such as LangChain, LangGraph, or RAG solutions.
• Experience within banking, financial services, or large enterprise environments.
• Tech Stack: Python, PySpark, SQL, Hadoop, Spark, Kafka, Azure, Databricks, Power BI/Tableau.

Interested applicants, please email your resume to Andre Chua Jing Ming

Email: andrechua@recruitexpress.com.sg

CEI Reg No: R1989053

EA Licence No: 99C4599

Recruit Express Pte Ltd

Responsibilities

• Develop and maintain ETL/ELT pipelines using Python, PySpark, and SQL.
• Build and support batch and real-time data processing solutions.
• Integrate data from multiple sources and ensure data quality and reliability.
• Monitor, troubleshoot, and optimize data pipelines and workflows.
• Collaborate with business, analytics, and technology teams to deliver data-driven solutions.
• Support cloud-based data platforms and data warehousing initiatives.
• Develop automation and monitoring solutions to improve operational efficiency.
• Any other ad-hoc duties as assigned by supervisor.

Requirements

• Bachelor's Degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline.
• 3+ years of Data Engineering or ETL development experience.
• Strong proficiency in Python, PySpark, and SQL.
• Experience with Hadoop, Spark, Kafka, or similar big data technologies.
• Knowledge of data warehousing and data modeling concepts.
• Experience with cloud platforms such as Azure Data Factory, Azure Synapse, Databricks, or Azure Data Lake.
• Strong problem-solving and stakeholder management skills.

Technical Skills

• Experience with Apache Flink or real-time streaming technologies.
• Exposure to Power BI, Tableau, Qlik, or other BI tools.
• Familiarity with AI/LLM frameworks such as LangChain, LangGraph, or RAG solutions.
• Experience within banking, financial services, or large enterprise environments.
• Tech Stack: Python, PySpark, SQL, Hadoop, Spark, Kafka, Azure, Databricks, Power BI/Tableau.

Interested applicants, please email your resume to Andre Chua Jing Ming

Email: andrechua@recruitexpress.com.sg

CEI Reg No: R1989053

EA Licence No: 99C4599

Recruit Express Pte Ltd

Contributes to the development of new analytics tool, software, and approaches, including the design, development, troubleshooting and debugging.

  *   Ongoing maintenance, update and support of the existing programming and analytic tools

  *   Develop, implement and maintain server and database infrastructure, hardware and software to ensure maximum tool uptime and performance.

  *   Execute end-to-end data processing workflows, ensuring data is accurately ingested, cleaned and structured across corporate systems.

  *   Manage and monitor data flows between current and future infrastructure components to maintain data integrity and consistency.

  *   Analyze processed data to uncover trends, patterns and operational performance metrics using statistical techniques.

  *   Exercises significant independent judgment to determine best method for accomplishing work and achieving objectives

  *   Leads and /or provides expertise to functional project teams and may participate in cross-functional initiatives.

The requirements:

  *   5 years of hands-on experience in data engineering and analytics

  *   Strong skills in statistical tools and programming languages such as Python, R, etc.

  *   Experience with data analysis / visualization tools and software like Power BI, Looker, Databricks etc.

  *   Proficiency in SQL for data extraction and manipulation.

  *   Attention to detail and a commitment to data accuracy.

  *   Proactive approach to identifying issues and providing solutions.

  *   Strong analytical and problem-solving skills. Ability to analyze complex datasets and translate data into actionable insights.

  *   Ability to present complex information in an understandable and compelling manner.

  *   Excellent written and verbal communication skills

  *   Ability to work independently and collaboratively as part of team

•You will be responsible for end-to-end delivery of enterprise data and analytics solutions leveraging traditional and modern Data architecture. Experience with Financial Crime Analytics, Finance & Credit Risk Analytics, Credit Scoring & Decision systems, Retail & Wholesale Datamarts will of advantage

•The role spans requirements analysis, solution design, testing, implementation, and production support ensuring high-quality, scalable, and compliant data platforms that support advanced analytics and AI initiatives.

Key Responsibilities

•Lead end-to-end solution delivery for data and analytics across the full SDLC.

•Analyze business and regulatory requirements, translate them into scalable solution designs & provide estimations.

•Communicate complex technical and architectural concepts to business and senior stakeholders in a clear, simplified manner.

•Review and approve test strategies, functional test cases, and data validation approaches.

•Manage risks and issues related to scope, data quality, regulatory commitments, and delivery timelines.

•Participate in product and platform evaluations (RFPs, PoCs) for data, analytics, and AI tooling.

•Partner with production support team to conduct root cause analysis, resolution, and preventive controls.

•Lead innovation and modernization initiatives, including data discovery, cataloguing, governance, and AI enablement.

•Drive productivity, efficiency & quality improvements across delivery and operational processes.

•Ability to design data architectures supporting NLP and AI-driven analytics.

FUNCTIONAL SKILLSETS

Analytics Domains

•Financial Crime Analytics Transaction Monitoring, Customer Due Diligence, Sanctions & Payments Screening

•Finance & Credit Risk Analytics Financial reconciliation, Allocation, Performance management, Regulatory and Management reporting, Credit risk exposure, NPL, Counterparty risk, Basel & IFRS9 input variables

Enterprise Data, Analytics & Unstructured Data Enablement

•Proven experience delivering large-scale analytics platforms within financial services spanning structured, semi-structured, and unstructured data

•Strong capability in requirements analysis and functional design for analytics use cases involving Transactional data, Investigator narratives, Case notes and alerts, Policy & Customer communications documents

•Experience defining data quality, governance, lineage, and reconciliation controls for both structured and NLP-derived datasets.

Unstructured Data & NLP-Enabled Analytics

•Ability to define data architectures and data flows that ingest, curate, and govern unstructured and semi-structured data within enterprise data platforms.

•Experience translating business requirements into NLP-enabled analytical use cases, such as Text classification and categorization, Entity & relationship extraction, Risk indicator identification, Summarization of alerts, cases, or documents

Knowledge Graph & Relationship Based Analytics

•Ability to design and govern an enterprise knowledge layer defining relationship taxonomies, entity resolution rules, and linkage logic

•Ability to translate use cases into relationship driven analytical designs, such as Network based risk identification, Hidden association and indirect exposure analysis, Related party and concentric risk detection

Requirements

Data Platforms & Architecture

•Open table formats: Apache Iceberg, Delta Lake, Apache Hudi

•Distributed processing & query engines: Spark, Trino/Presto, Hive

•Cost optimization strategies: tiered storage, lifecycle management, workload governance

Programming & Analytics

•SQL, BTEQ, GCFR

•Python (Pandas, NumPy)

•BI & visualization tools: Power BI, QlikSense

Data Integration & Quality

•Informatica suite: PowerCenter, BDM, IDQ, Enterprise Data Catalogue

•Data ingestion patterns: batch, CDC, streaming

•Data validation, quality controls, and reconciliation frameworks within environments

Governance, Risk & Compliance

•Data modelling, critical data elements, regulatory reporting

•Fine-grained data access controls (row-level, column-level, masking)

•Metadata management, lineage, and impact analysis

•Compliance with BCBS 239, MAS, AML/CFT, and internal data standards

Big Data Platforms

•Cloudera Hadoop distribution: Hive, Impala, Spark, Iceberg, Trino

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.

Candidates without prior experience are welcome to apply as on the job training would be provided.

Working hours :

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

Key Responsibilities

·      Lead the design and implementation of ESG data, reporting, and analytics solutions across the organization.

·      Define technical architecture, data models, and integration frameworks for ESG data platforms.

·      Collaborate with business stakeholders, sustainability teams, and technology teams to translate ESG reporting requirements into scalable technical solutions.

·      Provide technical leadership to data engineering, analytics, and data science teams throughout the solution lifecycle.

·      Drive the development of ESG dashboards, KPIs, scorecards, and performance monitoring frameworks.

·      Oversee the ingestion, transformation, validation, and management of ESG datasets, including carbon emissions, sustainability metrics, and regulatory reporting data.

·      Establish and enforce ESG data governance, data quality, metadata management, and reporting standards.

·      Ensure ESG solutions align with global reporting frameworks and regulatory requirements, including GRI, SASB, TCFD, ISSB, and other emerging standards.

·      Review technical designs, code, and implementation approaches to ensure scalability, reliability, and maintainability.

·      Lead system integration efforts across internal and external ESG data sources and reporting platforms.

·      Support advanced analytics initiatives, including predictive modeling, sustainability insights, and ESG risk analysis.

·      Manage technical delivery, project planning, risk management, testing, and production deployments.

·      Mentor technical team members and promote best practices in data engineering, analytics, and ESG technology solutions.

·      Act as the primary technical advisor for ESG-related initiatives and stakeholder engagements.

Required Skills and Experience

·      Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, Sustainability, Finance, or a related discipline.

·      5+ years of experience in data, analytics, or technology delivery roles, with at least 2 years in a technical leadership capacity.

·      Strong understanding of ESG concepts, sustainability reporting frameworks, and regulatory requirements.

·      Proven experience designing and delivering enterprise-scale data and analytics solutions.

·      Strong expertise in data architecture, data modeling, ETL/ELT processes, and data governance frameworks.

·      Hands-on experience with data visualization and analytics platforms such as Power BI or Tableau.

·      Proficiency in SQL and working knowledge of Python or R for data analysis and automation.

·      Familiarity with cloud-based data platforms and modern data engineering technologies.

·      Experience leading cross-functional teams within Agile delivery environments.

·      Strong knowledge of data quality, master data management, and reporting controls.

·      Excellent stakeholder management, communication, and leadership skills.

·      Ability to bridge business objectives with technical implementation and drive successful project outcomes.

Preferred Qualifications

·      Experience within financial services, banking, consulting, or sustainability-focused organizations.

·      Exposure to ESG data providers, sustainability reporting platforms, or regulatory reporting systems.

·      Knowledge of machine learning, predictive analytics, or advanced statistical modeling techniques.

·      Relevant certifications in ESG, sustainability reporting, cloud technologies, or data management are advantageous.

Job Duties & Responsibilities:

1. Work with large and complex financial datasets to develop end-to-end data science solutions for pricing various financial products, dynamic campaign optimization, and customer personalization.
2. Conduct research and literature review to assess and evaluate trade-offs between different quantitative algorithms and models.
3. Implement and train AI/ML models and optimize algorithm efficiency (GPU distributed computing, concurrent programming)
4. Refactor and document code into reusable libraries/ APIs/ tools, deploy machine learning ecosystems, and perform sub-system integration as required.
5. Integrate solutions into enterprise MLOps ecosystem and automate CI/CD pipelines for model lifecycle maintenance and monitoring.

Requirements:

1. Good understanding of the data science production life cycle with demonstrable experience working with structured, semi-structured and unstructured data.
2. Excellent software skills (Python, SQL variants) and knowledge in design patterns, code optimization, object-oriented programming.
3. Experience applying quantitative and machine learning algorithms for pricing and marketing.
4. Demonstrable expertise in some of the following domains - econometrics, statistical modelling, time-series analysis, signal processing, reinforcement learning, estimating causal relationships/ counterfactual effects, dynamic pricing.
5. Solid understanding of foreign exchange markets, including knowledge of currency pairs, market dynamics, and key drivers.
6. Hands-on experience in designing and executing digital campaigns and experimentation (A/B, Multivariate, Bandits, Sequential, quasi-experiments), evaluation methodologies (DiD variants), and conducting experiments to optimize campaign performance.

Act as the key local expert and lead user for planning-relevant master data, ensuring high consistency, quality and reliability of data used in Blue Yonder to support holistic planning of the semiconductor production network.

Serve as a critical interface between business, specialty departments, IT, and external IT service providers, driving further development, stabilization and continuous improvement of master data and planning IT solutions.

Utilize cost advantages of LCL.

• Drive end‑to‑end planning master-data quality and governance, ensuring reliable, usable data across business functions
• Act as technical lead for analytics and data solutions (Power BI, SQL Oracle/MSSQL, KNIME, databases, web/UI tools)
• Responsible for maintenance and enhancement of master data applications and UIs (e.g. Tivity) aligned with business needs
• Drive dashboards and advanced analytics to enable planning excellence, transparency, and self‑service insights
• Lead root cause analysis of complex data issues, implementing sustainable, preventive solutions
• Define, monitor and improve data governance frameworks, validation rules, workflows, KPIs and operating models
• Identify data quality trends and drive automation, structural improvements and scalability
• Ensure data solutions effectively support planning processes (demand planning, logistics interfaces, master data flows)
• Manage global and local stakeholder expectations, priorities and dependencies with clear, business‑oriented communication
• Motivate and develop the team by setting technical standards, best practices and ways of working
• Foster a culture of ownership, continuous improvement, analytical thinking and knowledge sharing
• Lead and contribute to PJM1‑SO projects, driving innovation, efficiency and advanced analytics adoption

Responsibilities:

• Data Infrastructure: Build, implement, and maintain scalable data pipelines and storage architectures.
• Data Quality: Maintain strong data integrity by ensuring reliability, consistency, and accuracy across all data processes.
• Performance Optimization: Enhance system efficiency by tuning data processing workflows and improving large-scale performance.
• Process Improvement & Mentoring: Strengthen data engineering practices while providing guidance and support to junior engineers.
• Cross-Functional Collaboration: Partner with business analysts, data scientists, and backend teams to deliver timely and actionable data solutions.

Qualifications:

• 5+ years’ experience in data engineering or backend roles, with strong programming skills in Python or Java/Scala.
• Hands-on experience with big data tools: Spark, Flink, Kafka, Airflow, and cloud platforms (AWS, GCP, Azure); proficient in SQL.
• Skilled in designing and maintaining data models, warehouses, and ETL/ELT pipelines for scalable, reliable systems.
• Knowledgeable in data quality, governance, security, and compliance best practices.

We regret only shortlisted candidates will be contacted. All applications will be handled confidentially. By submitting your application, you agree to the collection, use, retention, and disclosure of your personal information to prospective employers.

Wecruit Pte Ltd
EA License No: 20C0270
EA Personnel: Nyon Hock Sen (Aden)
EA Personnel Reg No: R24124070

·      Administer and manage Redis clusters for low-latency caching and real-time transaction processing.

·      Manage MongoDB clusters (replication, sharding) for scalable transaction and semi-structured data storage.

·      Working knowledge of Hadoop ecosystem (Hadoop, Hive, Pig, Oozie, Hbase, Flume, sqoop) using both automated tool sets as well as manual processes.

·      Support and maintain Hadoop (HDP)clusters for batch processing, analytics, and regulatory reporting.

·      Perform cluster lifecycle management provisioning, scaling, patching, and decommissioning nodes.

·      Ensure 24x7 availability and resilience of production systems supporting payment flows.

·      Manage and optimize Apache Kafka for high-throughput, real-time payment event streaming.

·      Support Apache NiFi for ingestionpipelines from upstream payment systems and external partners.

·      Work with AWS EMR for scalableprocessing of transaction data and reconciliation workloads.

·      Administer HDFS, ensuring optimal replication,storage utilization, and fault tolerance.

·      Manage OpenSearch / Elasticsearchclusters for transaction search, audit trails, and operational dashboards.

Required Skills & Experience

·      6–8 years of experience in Big Data / DataPlatform Engineering in enterprise environments.

·      Strong hands-on experience with:

·      Hadoop ecosystem (HDFS, YARN, MapReduce, HDP)

·      Apache Kafka (high-throughput environments)

·      Redis and MongoDB clusters

·      OpenSearch / Elasticsearch

·      Apache NiFi

·      AWS EMR + good knowledge of AWS Cloud.

·      Strong expertise in Linux system administration and scripting (Shell/Python)

·      Experience with Kerberos, data security, and access governance

·      Proven experience in handling high-volume, low-latency systems (preferably payments/trading) 

EA License No: 11C4879 / Registration ID : R1218583

Apar Technologies Pte Ltd, Singapore