Data Scientist

Cloud Kinetics; Indonesia; On-site; IDR 420,000,000 - 720,000,000; Full time

Job summary

Cloud Kinetics seeks a Middle to Senior Data Scientist & AI/ML Engineer to weave advanced analytics, ML engineering, and Generative AI into scalable business solutions in Jakarta. You will analyze data, build predictive models, deploy ML solutions, and create interactive dashboards using Streamlit, Tableau, or Power BI.

Proficiency in AWS SageMaker, Bedrock, and Lake House architecture is essential, as is experience with MLOps, data viz, and API development.

Qualifications

• BS/MS/PhD in CS/DS/AI or related field.
• 4–5+ years in Data Science and/or ML Engineering with AWS experience.
• Proficiency in Python (mandatory), SQL; Java/C++ optional.
• Experience with 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 onsite in South Jakarta with yearly contractextendable.

Responsibilities

• Perform EDA, statistical modelling, and feature engineering.
• Develop predictive and prescriptive models to drive business insights.
• Conduct A/B tests and hypothesis testing for model validation.
• Build dashboards using Power BI or Tableau to visualize insights.
• Design and train ML models using TF, PyTorch, and Scikit-learn.
• Implement ML pipelines with SageMaker Pipelines and Feature Store.
• Deploy scalable models via SageMaker endpoints, ECS, or EKS.
• Automate workflows with AWS MWAA and MLflow on AWS.
• Optimize model performance, inference speed, and real-time AI integrations.
• Build real-time AI apps with Streamlit; visualize outputs in BI tools.
• Develop APIs with FastAPI/Flask; deploy on ECS/EKS/Lambda.
• Create CI/CD pipelines using CodePipeline, CodeBuild, Terraform, GitHub Actions.
• Maintain data pipelines with SQL, PySpark, and AWS Glue.
• Explore Generative AI with Amazon Bedrock and LLMs.

Skills

Python

SQL

Java/C++

TensorFlow

PyTorch

Scikit-learn

PyCaret

SageMaker

Bedrock

Redshift

Glue

Lambda

MWAA

Athena

Step Functions

MLflow

Docker

Kubernetes

CI/CD on AWS

Tableau

Power BI

Streamlit

Education

Bachelor's degree

Master's degree

PhD

Tools

FastAPI

Flask

Terraform

GitHub Actions

Job description

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.