Lead Data Scientist

Job Purpose

• The Lead Data Scientist will lead the charge in EDOTCO's Data Science & Analytics team, recognized as the primary Centre of Excellence for all organisational analytics. This role encompasses a broader responsibility beyond reporting and analysis, extending to driving the strategic direction of advanced analytics across the company.
• Responsibilities are expanded to include the design and implementation of cutting-edge analytical solutions, pioneering data-driven strategies, and leading initiatives in areas such as finance, engineering, operations, and beyond. This includes the development of innovative dashboards, generating transformative insights, and crafting state-of-the-art predictive/prescriptive models to elevate business performance and decision-making processes.

Key Accountabilities

• Act as a pivotal leader within the Centre of Excellence, providing strategic and organisation-wide analysis while spearheading major analytical projects and initiatives.
• Conduct advanced data wrangling and exploratory analysis on extensive datasets from both internal systems and diverse external sources, harnessing this data to drive significant business improvements.
• Oversee and enhance workflow automation, including the design and execution of sophisticated data pipeline architectures, ensuring scalability and efficiency.
• Manage the end-to-end NAPA product, a geohash analytics platform designed to enhance customer engagement for nominal site planning and identify potential colocation of towers.
• Spearhead the deployment of generative AI use cases within the organization, driving innovation and leveraging advanced AI technologies to solve complex business problems.
• Address complex business challenges through in-depth analytics, engaging with key stakeholders at all levels to communicate methodologies, results, and recommendations, thereby influencing critical business decisions.
• Develop and implement advanced predictive and prescriptive models, elevating the role of analytics in strategic decision-making and achieving key project objectives.
• Mentor and support the professional growth of team members, fostering a culture of technical excellence and continuous learning. This involves leading code reviews, sharing expertise in both technical and domain-specific areas, and nurturing a collaborative environment.
• Lead cross-functional and inter-departmental problem-solving initiatives, providing comprehensive end-to-end solutions and driving innovation and excellence in analytics practices across the organization.

Qualification, Skills & Experience

• Advanced degree (Master’s or Ph.D.) in Mathematics, Statistics, Computer Science, or a related field, showcasing a strong foundation in quantitative analysis.
• A minimum of 10 years of extensive experience in data analytics or data science, with a significant portion in a leadership capacity, demonstrating a track record of leading high-impact projects and innovative solutions.
• Experience in key industries such as finance, telecommunications, operations, or location-based analytics is strongly preferred, with a focus on leading teams in these sectors.
• Mastery in programming languages such as Python, R, and SQL, with the ability to guide and review the work of others in these languages.
• Expert-level skills in data visualization, with proficiency in tools like MS Power BI and Tableau, and the capability to mentor others in these technologies.
• Comprehensive understanding and hands-on experience in advanced modelling, machine learning, and deep learning, especially in cloud computing environments.
• Outstanding communication skills, capable of effectively articulating complex data science concepts and strategies to both technical and non-technical stakeholders at all organizational levels.
• Proven ability to quickly grasp new technologies and concepts, with a track record of developing novel and effective solutions to complex and strategic problems.
• Preparedness for occasional domestic and international travel as part of a leadership role.
• A consistent history of professional growth and contribution in the field of data science, as evidenced by advanced certifications, leading-edge publications, or significant contributions to the data science community.