Senior/Lead Data Scientist
Job Overview
We are seeking a skilled and versatile Data Scientist with AI familiarity to join our growing team. In this role, you’ll collaborate with practice leaders, engineers, and cross-functional stakeholders to solve complex business challenges using data science and AI-driven approaches. You’ll work on end-to-end data science initiatives, with opportunities to design and implement cutting-edge generative AI (GenAI) and LLM-powered solutions.
Key Responsibilities
Data Science & Analytics
Partner with practice leaders and clients to understand business problems, industry context, data sources, risks, and constraints.
Translate business needs into actionable data science solutions, evaluating multiple approaches and clearly communicating trade-offs.
Collaborate with stakeholders to align on methodology, deliverables, and project roadmaps.
Develop and manage detailed project plans including milestones, risks, owners, and contingency plans.
Create and maintain efficient data pipelines using SQL, Spark, and cloud-based big data technologies within client architectures.
Collect, clean, and integrate large datasets from internal and external sources to support functional business requirements.
Build analytics tools that deliver insights across domains such as customer acquisition, operations, and performance metrics.
Perform exploratory data analysis, data mining, and statistical modeling to uncover insights and inform strategic decisions.
Train, validate, and tune predictive models using modern machine learning techniques and tools.
Document model results in a clear, client-ready format and support model deployment within client environments.
AI & Generative AI Collaboration
In addition to traditional data science responsibilities, you will collaborate with AI and engineering teams to:
Design and implement production-grade AI solutions leveraging LLMs, transformers, retrieval-augmented generation (RAG), agentic workflows, and generative AI agents.
Optimize prompt design, workflows, and pipelines for performance, accuracy, and cost-efficiency.
Build multi-step, stateful agentic systems that utilize external APIs/tools and support robust reasoning.
Deploy GenAI models and pipelines in production (API, batch, or streaming) with a focus on scalability and reliability.
Develop evaluation frameworks to monitor grounding, factuality, latency, and cost.
Implement safety and reliability measures such as prompt-injection protection, content moderation, loop prevention, and tool-call limits.
Work closely with Product, Engineering, and ML Ops to deliver robust, high-quality AI capabilities end-to-end.