Data Scientist

Exeevo is a global leader in customer relationship management (CRM) solutions, purpose-built for the Life Sciences industry. Our AI-powered, cloud-based platform unifies sales, marketing, medical, and service functions, enabling pharmaceutical, biotech, and medical device organizations to connect with stakeholders in transformative and innovative ways. By leveraging advanced technologies, Exeevo empowers Life Sciences companies to redefine how they engage with healthcare professionals, patients, and other key audiences, driving growth and delivering measurable business impact. Serving thousands of users across pharmaceutical and medical device companies globally, Exeevo has a strong international presence with offices in China, India, Europe, and North America.

About the Role : We are seeking a talented and driven Data Scientist to join our team in the life sciences industry. The ideal candidate will be responsible for a complete end-to-end data lifecycle, from ingesting raw data from Dataverse and external sources to building and deploying machine learning models in a production environment. The successful candidate will have a strong technical background, a deep understanding of data science fundamentals, and excellent communication skills.

Requirements

Responsibilities :

• Data Ingestion and Engineering : Acquire and process raw data from various sources, including Microsoft Dataverse and third-party applications. Design and implement robust data pipelines on the Azure platform to clean, transform, and prepare data for analysis and model training.
• Machine Learning Model Development & Deployment : Develop, train, and evaluate machine learning models for specific business problems, such as Next Best Action (NBA) and content recommendations. Utilize the Azure ML stack to deploy models into production, ensuring seamless and scalable consumption by Dynamics and custom applications.
• Collaboration & Communication : Work closely with clients to understand their data requirements and business needs. Use data visualization to communicate complex insights and model results to both technical and non-technical stakeholders. Collaborate with data engineers and product teams to ensure data quality, pipeline efficiency, and product alignment.

Skills and Qualifications :

Mandatory Skills & Experience :