Data Science and Reporting Section Head

Role Purpose

To lead a team of data scientists within the Analytics Department in order to develop advanced machine learning models, deliver actionable insights through corporate reporting, and oversee the creation of predictive models, advanced analytics, and data visualizations within the set KPIs, agreed budgets, and adopted policies and procedures.

Responsibilities for Internal Candidates

Key Accountability Areas

Key Activities

Operational Management

• Lead, mentor, and develop a team of data scientists
• Actively participate in on-the-job training, mentoring and coaching of subordinates
• Provide clear direction, prioritize tasks, assign and delegate responsibility and monitor the workflow
• Promote a high-performance working environment embracing SANS’s values
• Conduct regular performance reviews and provide coaching to team members
• Identify skill gaps and organize training programs to enhance team capabilities
• Provide recurring productivity reports on department progress and projects pipeline.
• Recruit, onboard, and retain top talent for the data science section

Budget Management

• Develop and manage budget and supporting business cases for the data science and reporting section including ML operations infrastructure.
• Justify budget requests to senior leadership by demonstrating the ROI of data science and analytics initiatives.
• Develop and negotiate contracts with tooling, infrastructure and service providers to optimize costs and service quality

Stakeholder and C-Suite Communication

• Generate and present regular operational and corporate performance insights and reports to senior leadership.
• Translate complex technical findings into actionable insights and strategic recommendations for non-technical audiences.
• Facilitate collaboration with IT and engineering stakeholders to ensure data science infrastructure needs are realized
• Build strong relationships with cross-functional teams to ensure alignment and collaboration on data-driven initiatives.
• Educate stakeholders on data science best practices.

Data science and ML products management

• Conducting discovery meetings to collect, analyze, clarify and document business requirements for data science and corporate reporting.
• Establish and monitor value metrics encompassing the full cycle of envisaged and developed data science products
• Continuously identify opportunities for data science products where value can be added to the business domain.
• Ensure that machine learning models and analytics solutions are scalable, reliable, and aligned with business needs.
• Manage advanced analytics workload including A/B and multivariate experiments, design and implement exploratory analysis and insight generation, all in close collaboration with partners across the Data management directorate and business groups.
• Collaborate with engineering and IT teams to integrate data science products into existing systems and workflows.
• Oversee the analysis of corporate and operational workstreams to provide periodic descriptive reporting and on-demand Diagnosis on key observed patterns for decision making purposes.
• lead the development efforts for provision of descriptive, predictive and diagnostic analysis for relevant constituencies.
• Develop strategy and roadmap for data science products while ensuring that defined path is consistent with the enterprise vision.
• Lead iterative data science products development process from idealization, through the implementation stage and all the way to the launch.
• Develop plans for ML models features/services delivery defining business goals, timelines, and roadmaps.
• Partner with business functions leads to proactively explore and overcome business problems through quantitative analytics levering established data science solutions.
• Support corporate planning and strategy functions in researching and analyzing industry operational and data related trends and developing market diligence periodically through quantitative reports and aggregation of relevant data from premium online/offline sources.
• Oversee the team’s execution of key technical activities including: Data exploration and pre-processing, statistical analysis and modeling, features engineering, models evaluation, data visualization and communication, and collaborative problem solving

Engineering management

• Participate in planning and executing workload related to deploying and expanding ML models hosting platforms at the back-end and front-end service layers
• Lead the deployment and configuration of developed data science solutions.
• Oversee the ML models development workload cadence includes establishment of and sustenance of devOPS and CICD practices and cadence, while ensuring engineering standards, including version control, code reviews, and testing protocols are maintained
• Develop, manage, and maintain engineering plans related to data science workload; work closely with enterprise and operations engineering teams.
• Implement agile methodologies and project management frameworks to streamline team operations and improve delivery timelines.
• Manage technical debt by prioritizing refactoring, documentation, and process improvements.

Policies, Processes and Procedures

• Support in monitoring day-to-day activities to ensure compliance with stipulated policies and procedures
• Contribute to the identification of opportunities for continuous improvement of systems and processes taking into account leading practices, changes in business environment, cost reduction and productivity improvement

People Management

• Actively participate in on-the-job training, mentoring and coaching of subordinates
• Provide clear direction, prioritize tasks, assign and delegate responsibility and monitor the workflow
• Promote a high-performance working environment embracing SANS’s values

Qualifications for Internal Candidates

Knowledge and Experience

• Minimum 6 years of experience in Data Science & Reporting, preferably leading and managing teams of data scientists and data analysts and budget management, resource allocation, and vendor negotiations.

Desirable:

• Referenced hands-on experience: team lead or unit manager/supervisor managing the operations and services of a data science team in a technology or engineering organization.
• Math / Quantitative skills: Skills with creating simulations, Erlangs theory for capacity planning, Hypothesis testing, Linear Regression, Logistic Regression, Outliers Detection, Linear Algebra with knowledge of Matrices attributes, knowledge and application of GLM (Generalized Linear Model)
• Strong scripting skills (e.g., Python, Bash) and knowledge of coding for creating automation scripts and integrating tools , and SQL/SPARK, JAVA/SCALA, Hadoop stack, C/C ++ for scripting/development on distributed/unstructured systems, and big data technologies such as Spark for handling and processing large datasets , and Python or PyTorch, Tensorflow, KERAS, Scikit-learn, XGBoost or similar stack for data manipulation, analysis, and data science modelling implementation.
• Familiarity or Ability to work with task management tools (JIRA, Microsoft Azure DevOPS boards), and Microsoft power platform , and hands on experience with popular SaaS services for database, developer and analytics (GCP, AWS or Azure)

Education and Certifications

• Bachelor’s Degree in Computer Engineering, Data Science, Computer science, Cybersecurity, Statistics, Engineering or equivalent is required.