Lead Data Engineer

Rakuten; Bengaluru; Presencial; INR 1 500 000 - 2 100 000; Tempo integral

Resumo da oferta

Rakuten is seeking a skilled Machine Learning Engineer in Bengaluru to drive real-time recommendation systems. You will work cross-functionally with product managers, data scientists, and engineers to build scalable models and data pipelines for production deployment.

The role requires strong knowledge of ML techniques, modern data science tools, and experience in the FinTech or credit card domain. Expect responsibilities including experimentation, model evaluation, and improving marketing

Qualificações

• Bachelor’s degree or higher in CS or a related field.
• Experience in FinTech or credit card domain preferred.
• Strong knowledge of ML techniques (classification, regression, clustering).
• Proficiency with data science tools and frameworks (Python, Scikit, Numpy, Pandas, TensorFlow/Keras).
• Production-grade software engineering: Git, CI/CD, and scalable code.

Responsabilidades

• Collaborate with product managers, data scientists, and engineers to define and implement ML solutions.
• Develop scalable algorithms for real-time recommendations.
• Experiment with new models and evaluate them with A/B tests.
• Build data pipelines for training and deploying models.
• Lead end-to-end ML system ownership from data pipelines to real-time inference.

Conhecimentos

ML techniques

Data-driven decision making

Experiment design

Formação académica

Bachelor's degree or higher in CS/related field

Ferramentas

Python

Scikit-learn

NLTK

NumPy

Pandas

TensorFlow

Keras

R

Spark

Databricks Delta Lake

MLflow

Azure

SQL

Descrição da oferta de emprego

Responsibilities:

• Work cross-functionally with product managers, data scientists, and product engineers, and communicate results to peers and leaders.
• Develop scalable algorithms and methods to provide real-time recommendations.
• Explore new technology shifts to determine how they might connect with the customer benefits we wish to deliver.
• Work with engineering teams to implement new models while considering functionality for recommendation output.
• Collaborate with data scientists to prototype new algorithms and design experiments for evaluation to enhance our marketing efficiency.
• Contribute to a culture of continuous improvement and data-driven results.
• Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models.
• Run regular A/B tests, gather data, perform statistical analysis, and draw conclusions on the impact of your models.
• Discover data sources and ingest and cleanse them in a meaningful way for data processing.
• Participate in design discussions about new features and approaches to implementing new services.
• Take end-to-end ownership of machine learning systems, from data pipelines and training to real-time prediction engines.
• Partner with data scientists to understand, implement, refine, and design machine learning and other algorithms.

Requirements:

• Bachelor's degree or higher (completed and verified before start) from an accredited university.
• Preferably working experience in the FinTech or credit card industry.
• Knowledgeable with data science tools and frameworks (i. e., Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, and Spark).
• Software engineering fundamentals: version control systems (i. e., Git, Github) and workflows, and ability to write production-ready code.
• Should have a clear understanding of generative AI and various LLM models.
• Databricks Delta Lake architecture and MLflow are added advantages.
• Good knowledge of Azure cloud infrastructure.
• Strong knowledge of machine learning techniques (i. e., classification, regression, and clustering).
• BS, MS, or PhD degree in computer science or a related field, or equivalent practical experience.
• Understand machine learning principles (training, validation, etc. ).
• Expertise in visualizing and manipulating big datasets.
• Knowledge of data query and data processing tools (i. e., SQL).