Staff Data Scientist

Roles and responsibilities

As part of the Data Science and Machine Learning (AI/ML) team you will be exposed to real-world challenges such as: dynamic pricing, predicting customer intents in real time, ranking search results to maximize lifetime value, classifying and deep learning content and personalization signals from unstructured data such as images and text, making personalized recommendations, innovating algorithm-supported promotions and products for supply partners, discovering insights from big data, and innovating the user experience. To tackle these challenges, you will have the opportunity to work on one of the world’s largest ML infrastructure employing dozens of GPUs working in parallel, 30K+ CPU cores and 150TB of memory.

In This Role, You’ll Get to

• Design, code, experiment and implement models and algorithms to maximize customer experience, supply side value, business outcomes, and infrastructure readiness
• Mine a big data of hundreds of millions of customers and more than 600M daily user generated events, supplier and pricing data, and discover actionable insights to drive improvements and innovation
• Work with developers and a variety of business owners to deliver daily results with the best quality
• Research discover and harness new ideas that can make a difference

Desired candidate profile

4+ years hands-on data science experience
Excellent understanding of AI/ML/DL and Statistics, as well as coding proficiency using related open source libraries and frameworks
Significant proficiency in SQL and languages like Python, PySpark and/or Scala
Can lead, work independently as well as play a key role in a team
Good communication and interpersonal skills for working in a multicultural work environment

It’s Great if You Have

PhD or MSc in Computer Science / Operations Research / Statistics or other quantitative fields
Experience in NLP, image processing and/or recommendation systems
Hands on experience in data engineering, working with big data framework like Spark/Hadoop
Experience in data science for e-commerce and/or OTA
Technical Expertise:

Proficient in programming languages commonly used in data science, including Python, R, and SQL. Knowledge of languages like Scala or Java is also beneficial.
Expertise in using machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn, Keras) and other relevant libraries for modeling and analytics.
Strong understanding of data wrangling and feature engineering techniques to prepare raw data for analysis and model training.
Experience with big data technologies (e.g., Hadoop, Spark, Hive) for processing large-scale datasets.
Familiarity with cloud-based tools and services for data science (e.g., AWS, Google Cloud Platform, Azure) and deployment.