Sr. Data Scientist

Job Responsibilities

Collaborating with business users to understand their key priorities and use cases; proposing and developing solutions using data science and/or Generative AI techniques to drive business value

Researching emerging AI/data science techniques and identifying relevant ones to explore and adopt (e.g. Agentic, LLM, Predictive, Fraud/Anomaly Detection, Text Analytics, Customer Segmentation)

Data wrangling & analysis - preprocessing, cleaning and feature engineering

Supporting the daily operations and maintenance of deployed data science models and products

Developing backend APIs and services to support AI model deployment and integration

Building frontend interfaces and user experiences for AI-powered applications

Documenting changes to existing products

Reviewing and implementing fixes for reported security vulnerabilities;

Able to understand and apply a range of AI/ML techniques for regression and classification

Familiar with popular python packages ( e.g. pandas, matplotlib, scikit-learn, XGBoost, NLTK, spaCy )

Understanding of LLM concepts (e.g. context windows, embeddings, chunking, token management) and architectures (e.g. RAG)

Experience with context engineering techniques and prompt optimization strategies

Proficient in git, SQL

Proficient in Business Intelligence tools (e.g. Tableau, Qlik, MS PowerBI, Microstrategy)

Bonus Skillsets

Proficient in modern programming languages (e.g. typescript, C#)

Experience in cloud platform and services preferably in AWS

Experience in Docker/Kubernetes

Experience with web frameworks and full-stack development, including backend frameworks (e.g. FastAPI, Flask, Express.js) and RESTful API development, and frontend technologies (e.g. React, Vue.js, HTML/CSS, TypeScript)

Experience with LLM frameworks (e.g. LangChain, LlamaIndex, Hugging Face Transformers)

Knowledge of vector databases and embedding techniques (e.g. Pinecone, Chroma, FAISS)

Understanding of AI agent frameworks and multi-agent systems

Strong presentation skills and ability to explain technical concepts clearly to a non-technical audience

Proficient in statistical software tools (e.g. R, SAS)

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