To address unmet medical needs by understanding complex and critical business problems, designing and applying integrated analytical approaches to explore data sources, and employing statistical methods and machine learning algorithms.
Requirements
- Strong programming skills in Python (with libraries like Scikit-learn, Pandas, and NumPy) and SQL
- Demonstrated knowledge of data visualization, exploratory analysis, and predictive modeling
- Proven experience with database technologies and a deep understanding of data lifecycle management
- Experience with AWS, Azure, or GCP and familiarity with big data frameworks like Spark or Hadoop
- Experience with advanced machine learning libraries such as TensorFlow, PyTorch, or Scikit Learn
- Familiarity with MLOps concepts and experience with tools for productionizing machine learning models
- Demonstrable knowledge and skills in one of the following domains: machine learning, deep learning, natural language processing (NLP), or the design of clinical trials
Responsibilities
- End-to-End ML Development: Design, build, and deploy machine learning models using AWS services (e.g., SageMaker, Lambda, EC2)
- Cloud-Based Data Engineering: Manage data pipelines and lifecycle using AWS tools; ensure clean, structured, and accessible data for analytics
- Dashboard Creation & Insights: Develop interactive dashboards using Amazon QuickSight to communicate insights and support decision-making across teams
- Business Problem Solving: Translate complex business questions into analytical frameworks and technical solutions
- Automation & Scalability: Build reusable components and automated workflows to reduce manual effort and accelerate analytics delivery
- Storytelling with Data: Present findings through compelling visualizations and contextual narratives tailored to diverse stakeholders
- Cross-Functional Collaboration: Partner with clinical study teams, product managers, and engineers to integrate data-driven insights into product development
Other
- MSc or PhD in Computer Science, Statistics, Machine Learning, Data Science or a similar quantitative discipline or demonstrable equivalent professional experience
- Experience of working collaboratively within multidisciplinary data science teams and delivering results in a timely way
- Skilled in business requirements analysis with ability to translate business information into technical specifications
- Ability to work across various business domains with high agility
- Ability to present findings through compelling visualizations and contextual narratives tailored to diverse stakeholders
