- Collaboration : Works closely with cross-functional teams and business stakeholders to develop roadmaps and shape solutions using standard technology stacks.

- Building Analytics Pipelines : Designs and oversees the development of analytics pipelines, products, and insights for the Commercial Pharma domain.

- Advanced Statistical Analysis : Utilizes advanced statistical analysis, machine learning techniques, and data visualization tools to uncover patterns, trends, and actionable insights from data.

- Business Impact : Synthesizes analysis and data into meaningful recommendations to drive concrete strategic decisions for brand tactics and commercial strategy.

- Innovation : Stays abreast of analytical trends and cutting-edge applications of data science and AI, championing the adoption of new techniques and tools.

- Quality and Governance : Ensures best practices in data management, model validation, and ethical AI, maintaining high standards of quality and compliance.

- Adaptability and Flexibility : Demonstrates the ability to adjust and thrive in changing environments, embracing new tasks, and applying knowledge in diverse contexts.

- Strong Communication : Exhibits effective communication skills for workplace interactions, including conveying ideas clearly and listening actively.

- Positivity : Maintains a positive attitude, showing a willingness to work hard and learn, contributing to a harmonious work environment.

- Self-Starter : Takes an active role in professional development; stays abreast of analytical trends and cutting-edge applications of data.

Requirements :

- Bachelor's, Master's, or PhD in Computer Science, Statistics, Data Science, Engineering, or a related quantitative field.

- 6-8 years of experience in Data Science, Analytics, or AI/ML roles, preferably in commercial pharma, healthcare, or related industries.

- Proficiency in programming languages such as Python, with exposure to OOPS concepts, Streamlit, Machine Learning, Deep Learning, and data visualization tools (including Plotly, Matplotlib, Seaborn, Tableau/Power BI, etc. ).

- Familiarity with databases such as SQL and NoSQL, and experience with data manipulation libraries (e. g., Pandas, Numpy) and machine learning frameworks (e. g., scikit-learn, PyTorch).

- Experience with large-scale distributed systems (e. g., PySpark, Snowflake).

- Strong understanding of statistical analysis, hypothesis testing, and other advanced statistical analytics techniques.

- Exposure to modern software development workflows (Git, CI/CD, Docker).

- Storyboarding and experience with MS Office, SharePoint.

- Familiarity with HCP engagement data, sales data, CRM platforms (e. g., Veeva), and omnichannel promotional metrics.

- Experience in dashboard development, storyboarding, and data product management.

- Ability to describe relevant caveats in data or models and how they relate to business questions.

- Experience with pharma data and commercial analytics is highly desirable.

- Track record of delivering business impact through analytics in pharma or related sectors.