Job Description

While the primary focus areas include AI strategy, automation, data-driven healthcare, and patient-centric innovations, the role is not limited to these areas.



Responsibilities may evolve based on emerging business needs, advancements in AI technology, and strategic priorities within the industry.



AI Strategy, Governance, and Compliance

Core Tasks & Outputs:

Develop and implement an AI strategy aligned with organizational goals.



Ensure compliance with regulations in AI deployments.



Establish ethical AI governance frameworks to ensure responsible AI use.



Lead change management initiatives to drive AI adoption across departments.



Conduct AI readiness assessments and recommend strategic improvements.

Key Outputs:

AI adoption roadmap and implementation plan.



Compliance reports and ethical AI assessments.



Training and awareness programs for AI adoption.



AI-Powered Automation & Process Optimization

Core Tasks & Outputs:

Identify manual processes that can be automated using AI.



Develop and deploy AI-driven workflow automation in clinical, research, and administrative functions.



Optimize supply chain operations through AI-based forecasting and logistics.



Ensure scalability and performance of AI solutions in cloud-based healthcare environments.



Integrate AI solutions with existing systems.

Key Outputs:

Automated workflows and process efficiency reports.



AI-powered supply chain forecasting models.



Deployment and integration of AI solutions into existing healthcare infrastructure.



AI for Data-Driven Decision-Making, Predictive Analytics & Commercial Strategy

Core Tasks & Outputs:

Develop AI-driven predictive analytics for patient risk assessment and disease prevention.



Leverage AI for drug discovery and clinical trial optimization, improving research efficiency.



Implement AI medical imaging and diagnostics models to support clinicians.



Enhance data processing and analysis capabilities for healthcare insights.



Use AI for sales, marketing, and prescription (Rx) forecasting to optimize commercial strategy.



Support marketing and sales teams with AI-driven customer segmentation, demand forecasting, and competitive intelligence.



Evaluate current data infrastructure and recommend solutions for data platform modernization to support AI initiatives.



Work with data engineering teams to optimize data pipelines, storage, and processing capabilities for AI-driven analytics.

Key Outputs:

AI models for predictive analytics in patient care.



AI-powered drug discovery reports and efficiency tracking.



Improved accuracy in AI-driven diagnostics and imaging analysis.



Sales & marketing forecasts using AI-driven predictive models.



AI-generated insights for commercial strategy, pricing, and market expansion.



Modernized data platform with optimized data pipelines for AI applications.



AI for Patient-Centric Innovation & Personalized Care

Core Tasks & Outputs:

Design AI-driven patient engagement tools (chatbots, virtual assistants).



Develop AI models for personalized treatment recommendations.



Utilize AI for remote patient monitoring and chronic disease management.



Enhance healthcare accessibility through AI-driven telemedicine solutions.



Work with healthcare providers to ensure AI enhances, rather than replaces, patient care.

Key Outputs:

AI-based virtual assistant for patient support.



Personalized treatment recommendation models.



AI-driven solutions for remote patient monitoring.



Education



Bachelor's degree in Artificial Intelligence, Data Science, Computer Science, Machine Learning, Engineering, Bioinformatics, Healthcare Informatics, or a related field.



Strong foundation in AI, machine learning, and data analytics.

Preferred (but not mandatory):

Master's degree or PhD in AI, Data Science, Healthcare Informatics, or a related discipline (especially for senior roles).



Certifications in AI/ML, Cloud Computing, or Data Science, such as:



Google Cloud AI/ML Certification

Microsoft Certified:

Azure AI Engineer Associate



AWS Certified Machine Learning - Specialty



TensorFlow Developer Certification



IBM AI Engineering Certification



Experience & Skills/Physical Competencies

Required Experience:

3-5 years of experience in AI, machine learning, data science, or related fields.



Hands-on experience in developing and deploying AI models.



Experience in predictive analytics, sales forecasting, and commercial strategy using AI.



Experience with data platforms, cloud environments, and database management.



Experience in automating business processes using RPA or AI-driven workflow tools.

Preferred experience (but not mandatory):

Exposure to healthcare and pharmaceutical industry standards, data privacy, and compliance.



Direct experience in AI applications for medical imaging, diagnostics, or clinical trials.



Prior work in AI-based patient engagement tools or personalized medicine.



Technical Skills

AI & Machine Learning:

Proficiency in AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn, Keras).



Experience in natural language processing (NLP), deep learning, and computer vision.

Programming & Development:

Strong knowledge of Python, R, SQL, and/or Java for AI model development.



Experience with big data technologies (Spark, Hadoop) is a plus.

Cloud & Data Platforms:

Experience in cloud-based AI deployment (AWS AI/ML, Azure AI, Google Cloud AI).



Hands-on experience with data pipelines, ETL processes, and real-time data processing.

Business & Analytical Skills:

Strong ability to analyze large datasets and provide data-driven business insights.



Ability to develop AI-driven commercial strategy, sales forecasting, and market analytics models.



Physical & Technical Competencies

Work Environment:

Ability to work in a fast-paced, technology-driven environment.



Flexibility to collaborate with cross-functional teams



Strong project management and multitasking skills.

Tools & Platforms:

Proficiency in BI and analytics tools



Experience in Robotic Process Automation (RPA) tools is a plus.



Behavioural Qualities

Problem-Solving & Innovation:

Strong analytical mindset with a proactive approach to identifying AI opportunities.



Ability to think critically and develop AI-driven solutions for business challenges.

Communication & Collaboration:

Strong ability to translate complex AI concepts into business-friendly insights.



Excellent written and verbal communication skills for stakeholder engagement.



Ability to collaborate with executives, data engineers, IT teams, and healthcare professionals.

Ethical AI & Compliance Awareness:

Strong understanding of ethical AI principles (bias mitigation, fairness, explainability).



Commitment to healthcare data security, privacy, and compliance.

Leadership & Adaptability:

Ability to lead AI initiatives and drive cross-functional alignment.



Willingness to adapt to evolving AI trends and business needs.



Between 3 - 5 Years