Job Description

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.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Statistics, or related field.

• 3+ years of hands-on experience in data science, machine learning, and statistical analysis.

• Proficiency in SQL, Python, Spark, and experience with cloud platforms (e.g., Azure, AWS, or GCP).

• Familiarity with Azure AI services, OpenAI models, and GenAI applications.

• Experience with model deployment and performance tuning in production environments.

• Strong communication and collaboration skills to engage with technical and non-technical stakeholders.

Preferred Qualifications

• Experience working in cross-functional teams with Product and Engineering.

• Exposure to LLM architectures, agent frameworks, and RAG systems.

• Understanding of AI safety, evaluation frameworks, and infrastructure considerations.

• Experience in healthcare, marketing, or similarly data-intensive industries is a plus.

Additional Information

Due to a high volume of inauthentic applications, we require a current and complete LinkedIn profile for consideration