Data Scientist (AI)
Data Scientist (AI)
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Job Description
We are seeking a skilled and versatile Data Scientist with AI familiarity to join our growing this role youll collaborate with practice leaders engineers and cross-functional stakeholders to solve complex business challenges using data science and AI-driven approaches. Youll 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 :
Bachelors or Masters 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
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
