Daniel Makanjuola

Objectives/Purpose:

• Lead the implementation and management of AI and GenAI data products in alignment with R&D and enterprise stakeholders.

• Ensure compliance with company and regulatory requirements.

• Provide machine learning expertise for complex and large-scale data projects.

• Act as a subject matter expert for machine learning frameworks in digital and innovation projects.

Accountabilities:

• Develop and implement data models and algorithms for pharmaceutical quality and R&D.

• Leverage generative AI and large language models to enhance data analysis and automate processes.

• Collaborate with cross-functional teams to ensure data integrity and accuracy.

• Provide expertise in designing and delivering digital projects.

• Set and maintain GxP standards in alignment with Software Development Lifecycle policies.

• Prepare and present detailed reports and visualizations to stakeholders.

• Lead the integration and advancement of AI technologies in R&D Quality.

• Develop and implement strategic AI initiatives to enhance Quality Management Systems (QMS).

• Oversee AI-driven projects and ensure timely delivery.

• Foster a culture of innovation and collaboration.

Dimensions and Aspects:

• Technical/Functional Expertise: In-depth knowledge of AI technologies in the pharmaceutical industry, experience with machine learning models, Agile methodologies, and GxPs.

• Leadership: Solution-oriented mindset, strong relationship-building skills, and effective communication.

• Decision-making and Autonomy: Provide input to complex decisions and ensure swift implementation.

• Interaction: Experience working cross-functionally and in matrixed, global teams.

• Innovation: Drive and support new ways of thinking and lead change.

• Complexity: High multicultural sensitivity and ability to navigate complex global ecosystems.

Education, Behavioral Competencies, and Skills:

• Master's degree in Data Science, Statistics, Computational Biology, Bioinformatics, Computer Science, or a related field with 8-12 years of experience.

• 5-10 years of experience applying machine learning/deep learning in life sciences.

• Proven track record of leading AI-driven projects in a pharmaceutical or biotechnology setting.

• Experience with generative AI and large language models.

• Familiarity with GxPs, regulatory requirements, and quality standards in the pharmaceutical industry.

• Strong problem-solving skills and attention to detail.

• Excellent communication skills to convey complex information to non-technical stakeholders.

Additional information-

Skills:

• Advanced Machine Learning Algorithms: Proficiency in implementing and optimizing machine learning algorithms using libraries such as Scikit-learn, TensorFlow, and PyTorch.

• Data Engineering: Experience with data extraction, transformation, and loading (ETL) processes, and familiarity with tools like Apache Spark and Hadoop.

• Data Modeling: Ability to design and implement data models that support business requirements and improve data accessibility.

• Statistical Analysis: Strong foundation in statistical methods and their application in real-world scenarios.

• Programming Languages: Proficiency in programming languages such as Python, R, and SQL.

• Data Visualization: Expertise in data visualization tools like Tableau, Power BI, or Matplotlib to present insights effectively.

• Domain Knowledge: Understanding of the specific industry domain to provide tailored, actionable insights.

Expectations:

• Collaboration: Work closely with cross-functional teams, including data engineers, business analysts, and stakeholders, to ensure alignment on project goals and deliverables.

• Communication: Effectively communicate complex technical concepts to non-technical stakeholders, ensuring clarity and understanding.

• Problem-Solving: Demonstrate strong problem-solving skills to address data-related challenges and provide innovative solutions.

• Continuous Learning: Stay updated with the latest advancements in data science and machine learning, and apply new techniques to improve existing processes.

Responsibilities:

• Develop Predictive Models: Create and refine predictive models to support business decision-making and improve operational efficiency.

• Conduct Advanced Statistical Analysis: Perform in-depth statistical analysis to uncover patterns, trends, and insights from large datasets.

• Oversee Junior Data Scientists: Mentor and guide junior data scientists, providing support and feedback to help them grow in their roles.

• Drive Business Insights: Use data-driven insights to inform strategic decisions and drive business growth.

• Ensure Data Quality: Implement data governance practices to maintain high data quality and integrity.

• Deploy and Monitor Models: Deploy machine learning models into production, monitor their performance, and make necessary adjustments to ensure optimal results.

• Innovate and Optimize: Continuously seek opportunities to innovate and optimize data science workflows and processes

• Company Overview: Nicosia, Cyprus
• Apply a variety of data analytics tools, including Lingo Software, University Teradata Analytics, and Tableau Solutions, to enhance business intelligence and optimization efforts
• Collaborate on projects to analyze variables and constraints, facilitating informed decision-making processes
• Contribute to both individual and team initiatives that drive data-driven strategies and solutions across enterprise-level applications
• Lead the implementation and management of AI and GenAI data products in alignment with R&D and enterprise stakeholders
• Ensure compliance with company and regulatory requirements
• Provide machine learning expertise for complex and large-scale data projects
• Act as a subject matter expert for machine learning frameworks in digital and innovation projects
• Develop and implement data models and algorithms for pharmaceutical quality and R&D
• Leverage generative AI and large language models to enhance data analysis and automate processes
• Collaborate with cross-functional teams to ensure data integrity and accuracy
• Provide expertise in designing and delivering digital projects
• Set and maintain GxP standards in alignment with Software Development Lifecycle policies
• Prepare and present detailed reports and visualizations to stakeholders
• Lead the integration and advancement of AI technologies in R&D Quality
• Develop and implement strategic AI initiatives to enhance Quality Management Systems (QMS)
• Oversee AI-driven projects and ensure timely delivery
• Foster a culture of innovation and collaboration
• Nicosia, Cyprus
• Technical/Functional Expertise: In-depth knowledge of AI technologies in the pharmaceutical industry, experience with machine learning models, Agile methodologies, and GxPs
• Leadership: Solution-oriented mindset, strong relationship-building skills, and effective communication
• Decision-making and Autonomy: Provide input to complex decisions and ensure swift implementation
• Interaction: Experience working cross-functionally and in matrixed, global teams
• Innovation: Drive and support new ways of thinking and lead change
• Complexity: High multicultural sensitivity and ability to navigate complex global ecosystems

• Develop comprehensive business intelligence solutions to support data-driven decision-making processes
• Utilize advanced analytics tools such as Python, Spark, and Snowflake to extract and manage complex data sets
• Apply expertise in information systems to optimize web-content management and enhance data usability
• Collaborate with cross-functional teams to align big-data analytics initiatives with organizational objectives

• Delivered comprehensive lectures in various computer science subjects, ensuring students met academic objectives and gained proficiency in complex concepts
• Published five articles in recognized international journals, contributing to the body of knowledge in applied mathematics and computer science
• Oversaw and guided post-graduate research projects, applying project management expertise to support student development and academic research initiatives

• Analyzed data and managed various projects, ensuring adherence to defined structures and constraints, contributing to informed decision-making processes
• Conducted software installations and upgrades, alongside hardware repairs, maintaining operational efficiency through meticulous inspections