Data Scientist

Cloud Kinetics Indonesia; Daerah Khusus Ibukota Jakarta; On-site; IDR 300.000.000 - 400.000.000; Full time

Job summary

A leading technology firm in Jakarta is seeking an experienced Data Scientist/ML Engineer to drive innovative AI/ML projects. The ideal candidate has a strong foundation in Python, extensive knowledge of AWS services, and a proven track record of delivering data-driven insights. Responsibilities include developing models, conducting analyses, and building dashboards for strategic decision-making. Join a collaborative team and help shape the future of AI-powered solutions.

Qualifications

• 4 – 5+ years in Data Science and/or ML Engineering with successful project delivery on AWS.
• Strong problem-solving, communication, and business acumen.

Responsibilities

• 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.
• Design and train ML models using TensorFlow, PyTorch, Scikit-learn.

Skills

Python

SQL

ML frameworks

Soft skills

Education

Bachelor's/Master's/PhD in Computer Science, Data Science, or related field

Tools

TensorFlow

PyTorch

AWS services

Docker

Kubernetes

Tableau

Power BI

Job description

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.
• Soft Skills: Strong problem-solving, communication, and business acumen.

Responsibilities

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.

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.

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.

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.

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.

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.

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.