We are looking for a Data Scientist who will understand complex and critical business problems, design and apply integrated analytical approaches to explore data sources, and employ statistical methods and machine learning algorithms to help address unmet medical needs. The role also involves discovering actionable insights and automating processes to reduce effort and time for repeated use. To manage the implementation and adherence to the overall data lifecycle of product data from data acquisition or creation through enrichment, consumption, retention, and retirement, enabling the availability of useful, clean, and accurate data throughout its useful lifecycle. High agility to be able to work across various business domains. High agility to be able to work across various business domains. Integrate business presentations, smart visualization tools and contextual storytelling to translate findings back to business users with a clear impact.
Requirements
MSc or PhD in Computer Science, Statistics, Machine Learning, Data Science or a similar quantitative discipline or demonstrable equivalent professional experience
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 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
Will be Plus
Demonstrable knowledge and skills in one of the following domains: machine learning, deep learning, natural language processing (NLP), or the design of clinical trials
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
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
Cross-Functional Collaboration: Partner with clinical study teams, product managers, and engineers to integrate data-driven insights into product development
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
