Data Science | SR

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Data ScienceData & AI

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Requirements Summary

Solid professional experience with Python and strong practical knowledge of data science and machine learning.

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Data ScienceData & AI

This is a senior-level opportunity for a Data Scientist to build advanced machine learning and Generative AI solutions with measurable business impact.
You’ll own projects end-to-end, from data exploration and experimentation through model validation, deployment, and interpretation.
The role combines traditional machine learning with LLMs, RAG, agent-based architectures, and modern AI evaluation techniques.
You’ll work extensively with Python, Databricks, MLflow, AWS, Azure, LangChain, and LangGraph in a highly technical environment.
You’ll also help develop scalable data and AI pipelines, APIs, vector-search solutions, and cloud-native architectures.
The position offers the opportunity to work on complex problems where experimentation, engineering excellence, and practical business outcomes go hand in hand.
It is well suited to a senior professional who enjoys solving challenging problems and working at the forefront of AI and data science

Develop predictive models and machine learning algorithms to solve complex business challenges, taking ownership from data exploration and experimentation through validation and interpretation, with a focus on measurable outcomes.
Apply supervised, unsupervised, and deep learning techniques using Python and leading data science libraries, including Scikit-learn, XGBoost, and LightGBM.
Design and execute experiments using tools such as Jupyter, Databricks, and MLflow, ensuring robust model evaluation and reproducible experimentation.
Evaluate the performance of RAG and language-model-based systems using quality, cost, latency, and semantic metrics, including BLEU, ROUGE, and related evaluation approaches.
Build text-ingestion pipelines for vector databases, including solutions using AWS OpenSearch, and develop APIs with LangChain and LangGraph integrated with OpenAI and AWS Bedrock.
Work with AWS services such as S3, Lambda, Parameter Store, and Secrets Manager, while contributing to serverless and containerized architectures across AWS and Azure.
Leverage Azure Document Intelligence and other cloud services for document extraction, processing, and AI-powered workflows.

Requirements

~1 min read

Solid professional experience with Python and strong practical knowledge of data science and machine learning.
Hands-on experience developing and deploying machine learning models using libraries such as Scikit-learn, XGBoost, and LightGBM.
Proven experience with Large Language Models (LLMs), Generative AI, RAG architectures, and agent-based AI solutions.
Practical experience working with Databricks and MLflow for data science, experimentation, and model development workflows.
Experience with LangChain and LangGraph for building LLM-powered applications and agentic solutions.
Experience working with AWS Bedrock and familiarity with the broader AWS ecosystem.
Knowledge of language-model evaluation metrics such as BLEU and ROUGE is considered a plus.
Working knowledge of both AWS and Azure cloud environments, including cloud-native, serverless, and containerized architectures.
Strong analytical and problem-solving abilities, with the capacity to translate complex technical challenges into practical solutions.
Ability to work independently, communicate technical concepts clearly, and collaborate effectively with multidisciplinary teams.