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Open nowPosted today

Data Scientist

MyCareersFuture91,045 open roles

Pay
SGD 10,000 – SGD 13,000 a Monthly
Where
Central, Singapore
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Your applicationOpen nowData ScientistMyCareersFuture · Central, Singapore

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  4. CV RocketApplied on MyCareersFuture's own form.

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The clock on this job

Early applications get read.

7.8% of postings close within 7 days. Measured by our own scanner across the market. MyCareersFuture postings stay open a median of 1 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.4%3 days
  3. 7.8%7 days
  4. 14.3%14 days
  5. 33.7%30 days
This job: posted today

MyCareersFuture median: 1 days open

The posting

Key Responsibilities

  • Design, develop and maintain scalable ETL/ELT data pipelines for large-volume structured and unstructured datasets.
  • Develop high-performance data processing solutions using Python, PySpark, Apache Spark, Spark SQL and Scala.
  • Build batch and real-time data pipelines using Kafka, Kinesis, Spark Streaming, AWS Glue and Airflow.
  • Develop data ingestion frameworks integrating RDBMS, APIs, files, cloud storage and streaming platforms.
  • Design and implement data lakes, data warehouses and cloud-based data processing platforms.
  • Work with Databricks, Hadoop, Hive, Cloudera, Presto and Snowflake for large-scale data processing and analytics.
  • Perform data modelling, data transformation, data quality, query optimisation and performance tuning.
  • Develop and optimise SQL solutions across Oracle, SQL Server, PostgreSQL, Teradata, MongoDB and cloud databases.
  • Design and implement data migration solutions involving large-scale enterprise datasets.
  • Develop and support real-time and batch processing architectures for enterprise applications.
  • Integrate data platforms with REST APIs, GraphQL and enterprise applications.
  • Implement CI/CD and DevOps practices using Jenkins, Git, Docker, Kubernetes and OpenShift.
  • Develop cloud-native data solutions using AWS and Azure, including S3, Glue, EMR, Redshift, Kinesis, Lambda, RDS and DynamoDB.
  • Develop AI/GenAI-enabled data solutions involving LLMs, NLP, RAG, Agentic AI and vector databases.
  • Integrate LLM services and AI platforms such as Azure OpenAI, OpenAI APIs, Hugging Face and Google Gemini/ADK.
  • Develop NLP pipelines for text processing, embeddings, summarisation, sentiment analysis, voice-to-text and speaker diarisation.
  • Design and implement vector search and retrieval solutions using Redis, ChromaDB and FAISS.
  • Develop AI-powered APIs and applications using FastAPI, Gradio and Python.
  • Collaborate with architects, data scientists, software engineers, business analysts and product teams to deliver enterprise data solutions.
  • Participate in Agile SDLC activities including requirements analysis, architecture, development, testing, deployment and production support.
  • Troubleshoot complex data, application and platform issues and provide scalable technical solutions.

Required Technical Skills

Data Engineering: Python, PySpark, Apache Spark, Spark SQL, Scala, Hadoop, Hive, Kafka, Presto, Databricks, Cloudera, Snowflake

Cloud Technologies: AWS, Azure, S3, Glue, EMR, Redshift, Kinesis, Lambda, RDS, DynamoDB, OpenSearch

Databases: SQL Server, Oracle, PostgreSQL, Teradata, MongoDB, Redis

Programming: Python, Java, Scala, SQL, Shell Scripting, Node.js

AI / GenAI / NLP: Generative AI, LLM, NLP, RAG, Agentic RAG, LangChain, LangGraph, LlamaIndex, Hugging Face Transformers, Azure OpenAI, OpenAI API, Google Gemini/ADK, PyTorch

Vector & AI Search: Redis Vector Database, ChromaDB, FAISS, Embeddings, Hybrid Search, Semantic Search

DevOps & Deployment: Docker, Kubernetes, OpenShift, Jenkins, Git, Terraform, CI/CD

Data Integration & APIs: REST APIs, GraphQL, FastAPI, API Gateway, AWS Lambda, CDC, Debezium

Data Visualisation: Power BI, Data Modelling, Reporting and Analytics

Qualifications

  • Bachelor's or Master's degree in Computer Science, Information Technology, Programming & Systems Analysis, Computer Studies or a related discipline.
  • Strong professional experience in Big Data, Cloud Computing or related technology domains.
  • Experience working with large-scale enterprise data platforms and production data pipelines.
  • Strong programming and SQL skills.
  • Experience with cloud-based data engineering and modern data processing frameworks.
  • Experience with AI/ML, NLP or Generative AI will be highly advantageous.

Preferred Experience

  • Enterprise Banking / Financial Services experience.
  • Experience working with large-scale customer, transaction or financial datasets.
  • Experience with data migration and legacy ETL modernisation.
  • Experience implementing AI/GenAI solutions within enterprise data platforms.
  • Experience with production deployments and CI/CD environments.
  • Strong understanding of data governance, security, data quality and performance optimisation.
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