Head of AI and Data Science

The Head of AI and Data Science will be a visionary leader with strong technical skills, capable of driving strategic direction, managing talent, delivering innovative solutions, and collaborating effectively across diverse stakeholder groups. The successful candidate will have proven experience in building and leading high-performance data science teams and delivering impactful results within complex organizations.

Key Responsibilities:

• Vision & Strategy:
  Develop and execute a comprehensive strategy for AI and Data Science across all functional areas, aligning it with the overall business objectives and digital transformation roadmap.
• Talent Development & Management:
  Build and lead a high-performance team of data scientists, machine learning engineers, and other relevant professionals, fostering collaboration, continuous skill development, and career progression within the organization.
• Technical Excellence:
  Maintain technical depth and expertise in AI, Machine Learning, Deep Learning, Data Warehousing, ETL, Cloud Computing technologies, and emerging trends to stay ahead of industry developments.
• Innovation & Research:
  Drive innovative research initiatives, collaborate with R&D teams, academic institutions, and external partners to identify new opportunities for AI and Data Science applications within the organization.
• Business Partnership:
  Serve as a strategic advisor to business leaders, understanding their needs and challenges, and developing data-driven solutions that drive business value creation and differentiation.
• Solution Delivery:
  Ensure successful delivery of AI and Data Science projects by setting clear objectives, defining project scope, managing timelines, budgets, and stakeholder expectations, while maintaining high standards of quality and impact measurement.
• Knowledge Management & Intellectual Property (IP):
  Develop processes for knowledge sharing, IP creation and protection within the team, contributing to the organization's overall intellectual capital development.
• Operational Excellence:
  Implement best practices in AI and Data Science operations, including data management, model governance, monitoring, and maintenance processes to ensure sustainability and scalability of solutions.
• Performance Management & Metrics:
  Establish key performance indicators (KPIs) for the team's success, regularly reviewing progress, and making necessary adjustments to improve performance and impact on business outcomes.
• Stakeholder Management:
  Effectively communicate the value proposition of AI and Data Science to various stakeholders within the organization, fostering buy-in, collaboration, and support for initiatives.

Qualifications:

• Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics or a related field.
• Advanced degree (Master's/Ph.D.) or equivalent experience in AI, Machine Learning, Data Science, or relevant fields preferred.
• Minimum of 10 years of experience in leadership roles within the technology industry, with demonstrated success in building and managing high-performance teams.
• Proven track record of delivering data science solutions that drive business value creation across multiple industries or functional areas.

Skill Requirements

• Must have strong programming experience with Python/R and SQL.
• Experience in machine learning, deep learning, data visualization, statistical, text analytics libraries, jupyter notebook and/or frameworks in Python or R.
• Solid understanding of statistical concepts and techniques for hypothesis testing, regression analysis, time series analysis, and predictive modeling.
• Experience with data wrangling and preprocessing such as data cleaning, feature engineering, and handling missing values.
• Experience in supervised, unsupervised learning, ensemble methods, deep learning and evaluation of machine learning algorithms.
• Experience in scikit-learn, numpy, pandas, seasborn, matplotlib, ggplot, deep learning framework: tensorflow, keras or pytorch.
• Experience in public cloud infrastructure such as AWS and/or Google Cloud Platform for high performance computing.
• Experience in developing and deploying applications running on public cloud infrastructure.
• Experience in Git for code management.
• Excellent written and verbal communication skills for coordinating across teams.
• Demonstrated experience applying a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Experience visualizing/presenting data for stakeholders using: Tableau, D3, ggplot is a plus.
• Experience in performing distributed data analysis on large data set will be an added advantage.
• Drive to learn and master new technologies and techniques.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
• Experience visualizing/presenting data for stakeholders using: Tableau, D3, ggplot is a plus.
• Experience in performing distributed data analysis on large data set will be an added advantage.
• Drive to learn and master new technologies and techniques.