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Data Scientist II
Hybrid · Hyderabad, Telangana, India
Job Summary
Hands-on data scientist or ML engineer responsible for ideating, designing, developing, modeling and deploying advanced data-science solutions. Collaborate with stakeholders to define objectives and KPIs, gather and preprocess large-scale structured/unstructured data from diverse sources (clinical trials, patient records, genetic data), apply statistical analysis and ML algorithms to develop actionable models, conduct exploratory data analysis to identify patterns, implement predictive models and AI/GenAI solutions, ensure data quality and availability with engineers, and communicate findings through visuals and presentations. Mentor junior data scientists and stay current with methodologies in biopharma data science, ML, and GenAI trends; familiar with cloud platforms and big data tech; experience with data visualization, experimental design, and rigorous evaluation frameworks; ready to work in a fast-paced environment with a focus on transforming patient outcomes.
Required Qualifications
- Bachelor's, Master's or Ph.D. degree in Data Science, Statistics, Computer Science or related field
- Proven hands-on experience in data science
- Experience with large-scale structured and unstructured data
- Experience with predictive modelling and ML algorithms
- Experience with healthcare/biopharma data desirable
- Experience with AI/GenAI technologies a plus
Desired Qualifications
- Proven experience (typically 2 to 5 years) in a data science role
- Experience with SQL, NoSQL or similar technologies
- Experience with cloud platforms (e.g., AWS, Azure)
- Experience with Python, PySpark or R for data manipulation, analysis and modeling
- Experience with data visualization tools (Tableau, Power BI, matplotlib/seaborn)
- Strong understanding of experimental design, hypothesis testing and A/B testing
- Excellent problem-solving and communication skills
- Experience working with healthcare data, clinical trials, or related domains is highly desirable
- Exposure to AI/GenAI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) frameworks, Agentic AI architectures, prompt engineering
- Experience with building and deploying data science and data engineering solutions (MLOps, Git)
- Familiarity with data sources in biopharma or pharmaceutical industry
- Experience with SQL-based and NoSQL data stores
- Mentoring or leadership experience could be beneficial
- Knowledge of data governance, regulatory considerations in biopharma
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