Position Summary:
- Lead DS and ML predictive analytics solutions, owning technical strategy and delivery for data science projects of varying complexity.
- Serve as technical lead for data science squads — driving architecture decisions, code quality standards, and delivery excellence across the team.
- Bridge business and technical teams by translating complex analytical findings into clear, actionable insights that influence strategic decisions.
- Foster a culture of continuous learning by mentoring data scientists, promoting best practices in MLOps, and championing innovation in analytical methods
Accountabilities:
Model Development & Analytics Leadership
- Build and deploy predictive models and analytics capabilities, leading technical workstreams independently
- Perform advanced data analysis and EDA to develop fact-based insights and strategic recommendations
- Lead integration of traditional and nontraditional data sources into production analytical workflows
- Drive integration of structured and unstructured data sources into robust, scalable analytical pipelines.
Platform and MLOps
- Architect and maintain ML infrastructure using cloud platforms (Databricks, AWS, Azure) and orchestration tools.
- Implement CI/CD practices for model deployment, versioning, and automated retraining pipelines.
- Establish model monitoring, drift detection, and performance tracking frameworks in production.
- Promote reusable components, shared libraries, and standardised development environments across the team.
Leadership & Stakeholder Management
- Act as technical point of contact for stakeholders — translating business problems into clear data science problem statements.
- Lead cross-functional working groups with business owners, data engineers, and domain experts.
- Mentor and develop junior and mid-level data scientists through code reviews, knowledge sessions, and project coaching.
- Facilitate agile ceremonies (sprint planning, retrospectives, technical demos) and hold the team accountable to delivery commitments.
Strategic Contribution
- Identify and prioritise high-impact AI/ML opportunities across Commercial domains (Marketing, Sales, Pricing).
- Build and share domain expertise, contributing to the development of team capability roadmaps.
- Influence tooling and platform strategy decisions based on emerging best practices and business requirements.
- Drive measurable business outcomes — framing model outputs in terms of ROI, efficiency gains, or revenue impact.
- Bachelor's degree in Computer Science, Mathematics, Statistics, Data Science, or related quantitative field (Master's preferred)
- 3-5 years professional experience delivering production data science solutions
- Proven track record leading analytical projects with measurable business impact
- Advanced proficiency with Python ecosystem (scikit-learn, TensorFlow/PyTorch, pandas, numpy)
- Strong SQL skills and experience with cloud data platforms (AWS, Azure, Databricks)
- Experience with ML frameworks, version control (Git), and CI/CD for model deployment
- Demonstrated leadership through mentoring, technical presentations, or cross-functional project ownership
Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status.
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