Solving complex business challenges using data science and AI-driven approaches at the company
Requirements
- 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.
- 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.
Responsibilities
- Partner with practice leaders and clients to understand business problems, industry context, data sources, risks, and constraints.
- Create and maintain efficient data pipelines using SQL, Spark, and cloud-based big data technologies within client architectures.
- 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.
- Design and implement production-grade AI solutions leveraging LLMs, transformers, retrieval-augmented generation (RAG), agentic workflows, and generative AI agents.
- Deploy GenAI models and pipelines in production (API, batch, or streaming) with a focus on scalability and reliability.
Other
- 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.
- Strong communication and collaboration skills to engage with technical and non-technical stakeholders.
- Ability to work closely with Product, Engineering, and ML Ops to deliver robust, high-quality AI capabilities end-to-end.
- Ability to collaborate with stakeholders to align on methodology, deliverables, and project roadmaps.
