Data Scientist

We are seeking a Middle or Senior Data Scientist & AI/ML Engineer who can seamlessly integrate advanced analytics, machine learning engineering, AWS-native AI/ML platforms, and Generative AI into business solutions. In this role, candidate will be responsible for data analysis, predictive modelling, AI/ML deployment, and interactive AI dashboards using Streamlit, Tableau or Power BI. Candidate will work with AWS SageMaker, Amazon Bedrock, and the AWS Lake House architecture to build scalable, AI-powered enterprise applications. This position requires deep experience in AWS native AI/ML services, AWS Lake House for unified data platforms, and MLOps automation.

Requirements:

• Education: Bachelor's/Master’s/PhD in Computer Science, Data Science, AI, or a related field.

• Experience: 4 – 5+ years in Data Science and/or ML Engineering with successful project delivery on AWS.

• Programming: Python (must-have), SQL, Java/C++ (optional).

• ML Frameworks: TensorFlow, PyTorch, Scikit-learn, PyCaret.

• AWS Services: SageMaker, Bedrock, Redshift, Glue, Lambda, MWAA, Athena, Step Functions.

• MLOps: MLflow, Docker, Kubernetes, CI/CD on AWS.

• Data Viz: Tableau, Power BI, Streamlit.

• Willing to do onsite working in South Jakarta area with yearly contract extendable.

Responsibilities:

1. Data Science & Advanced Analytics

• Perform exploratory data analysis (EDA), statistical modelling, and feature engineering.

• Develop predictive and prescriptive models to drive business insights. • Conduct A/B testing and hypothesis testing for model validation.

• Build interactive dashboards using Power BI or Tableau to generate business insights.

2. Machine Learning Engineering & MLOps

• Design and train ML models using TensorFlow, PyTorch, Scikit-learn. • Implement ML pipelines using Amazon SageMaker Pipelines, Feature Store, and AWS Step Functions.

• Deploy scalable ML models using SageMaker endpoints, ECS, or EKS. • Automate workflows using Amazon MWAA (Managed Airflow) and MLflow on AWS.

• Optimize model performance, inference speed, and real-time AI integrations.

3. AWS Lake House for Unified Data Platform

• Utilize AWS Lake Formation, Amazon Redshift, Glue, and Athena for unified data access and processing.

• Build and optimize end-to-end ML pipelines integrated with the AWS Lake House ecosystem.

• Collaborate with Data Engineers for seamless data ingestion, transformation, and governance using Glue ETL and DataBrew.

4. Interactive AI Applications & Data Visualization

• Build real-time AI-powered applications using Streamlit.

• Design dashboards with Power BI and Tableau to visualize AI/ML outputs. • Integrate ML outputs with BI platforms through Redshift or S3/Athena connectors.

5. Software Engineering & Deployment

• Develop APIs using FastAPI or Flask to expose ML services.

• Deploy solutions on ECS, EKS, or AWS Lambda.

• Build automated CI/CD pipelines using CodePipeline, CodeBuild, Terraform, and GitHub Actions.

• Maintain data pipelines using SQL, PySpark, and AWS Glue.

6. Generative AI & NLP Exploration & Development

• Use Amazon Bedrock to develop Generative AI applications with foundation models (e.g., Anthropic, Cohere, Meta).

• Fine-tune and optimize large language models (LLMs) for text generation, summarization, and chatbots.

• Integrate LLMs with business workflows using AWS API Gateway, Lambda, and other AWS cognitive services.

• Implement prompt engineering, embeddings with Amazon Titan, and retrieval augmented generation (RAG) on AWS.

7. Business Collaboration & AI Strategy

• Collaborate with business users, engineers, and stakeholders to define the AI/ML roadmap.

• Translate business needs into AI-powered solutions.

• Communicate insights through effective data storytelling and structured documentation.

• Stay informed on AWS AI/ML advancements and enterprise AI trends.