Data Scientist

Role Overview and Responsibilities

The Data Scientist will be responsible for building sophisticated statistical and machine learning models to analyze extremely large datasets, specifically petabyte-scale customer data, with the goal of extracting insightful patterns and valuable business intelligence. This role involves designing and executing carefully controlled experiments such as A/B tests and causal inference studies to gauge the impact of various strategies, initiatives, and product updates on user behavior and business metrics. Additionally, the candidate will develop and maintain robust MLOps pipelines to streamline the deployment, monitoring, and upkeep of machine learning models in production settings. Beyond technical execution, the role requires effective communication of complex analytical results to both technical teams and business stakeholders. Staying abreast of cutting-edge advancements in AI, particularly generative AI, is also a key aspect. The position also includes building scalable Big Data pipelines supporting data science workflows.

Qualifications and Requirements

• Bachelor’s degree or higher in Statistics, Applied Mathematics, Computer Science, or a related domain.
• Between three to five years of professional experience in the realms of data science, machine learning, and deep learning.
• Comprehensive knowledge of machine learning and deep learning theories and algorithms.
• Proven hands-on experience with building and deploying machine learning or deep learning models in live production environments.
• Proficiency with analytical tools including SQL and Python.
• Familiarity and comfort working with very large datasets and big data frameworks such as Hadoop, Spark, and Hive.
• Experience utilizing deep learning libraries, particularly TensorFlow and PyTorch.
• Strong expertise in A/B testing methodologies with demonstrated ability to optimize best practices.
• Self-motivated, with a readiness to both mentor peers and absorb new technologies independently.
• Knowledge and experience with generative AI tools and libraries such as HuggingFace, LangChain, and Retrieval-Augmented Generation (RAGAS) are highly advantageous.
• Practical experience fine-tuning large language models (LLMs) and using retrieval augmented generation techniques is a plus.
• Ability to thrive under pressure, tackling challenges head-on with resilience and determination.