Responsibilities
- Create statistical models and advanced analytics to address intricate business challenges.
- Conduct exploratory data analysis, feature creation, and hypothesis validation across large, varied datasets.
- Utilize machine learning methods including regression, classification, clustering, forecasting, and anomaly detection.
- Construct, train, assess, and refine machine learning models for real-world business deployment.
- Develop and maintain Generative AI systems using large language models, embeddings, prompt engineering, and retrieval-augmented generation.
- Assist in deploying and monitoring AI and ML models in coordination with engineering and MLOps teams.
- Process both structured and unstructured data to prepare high-quality datasets for analysis.
- Design dashboards, visualizations, and reports to clearly convey data insights to non-technical audiences.
- Collaborate with data engineers to enhance data pipelines and analytical processes.
- Engage with business teams to understand needs and uncover data-driven opportunities.
- Convert analytical results into practical business actions and strategic guidance.
- Aid executive decision-making through effective data storytelling and high-level presentations.
- Keep updated on advancements in data science, machine learning, artificial intelligence, and generative AI.
- Advocate for best practices in experimentation, reproducibility, documentation, and model management.
- Guide junior team members and support internal knowledge transfer.
