Analytics & Data Science Lead

Join Syngenta Group, a leader in agricultural innovation where technology meets purpose. As digital pioneers in AgTech, we're integrating AI across our value chain from smart breeding to precision agriculture. Our global team of 56,000 professionals is transforming sustainable farming worldwide. At Syngenta IT & Digital, your expertise will directly impact food security and shape the future of agriculture through cutting-edge technology.

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Purpose 

The Analytics & Data Science Lead is responsible for leading a team of Data Scientists, driving the evolution and maturity of Data Science, AI/ML capabilities to generate impactful insights and enable data-driven decision-making across corporate functions. This role acts as a key partner for our delivery squads and business stakeholders, translating strategic needs into robust, scalable, and well-governed data science solutions. The incumbent will be accountable for the end-to-end design, development, deployment, and user experience of advanced analytics and AI/ML products, ensuring their successful implementation and demonstrable value realization. This includes acting as a functional Single Point of Contact (SPOC) for at least one corporate function, leading dedicated data and analytics delivery squads to achieve business outcomes.

Accountabilities

Team Leadership & Capability Development:

• Drive the evolution and maturity of AI/ML capabilities within the Corporate Functions in close alignment with our central Data Science and AI teams.
• Lead, mentor, and manage a team of Data Scientists, fostering a culture of innovation, continuous learning, and high performance.
• Establish and mature MLOps and ML Engineering capabilities, both from a technical implementation and human capability standpoint, to ensure robust, scalable, and maintainable AI/ML solutions.
• Champion the design of AI/ML-ready data products and pipelines, optimized for consumption by Data Scientists and AI agents, within a Data Mesh framework.

Strategic Partnership & Solution Delivery:

• Act as a functional Single Point of Contact (SPOC) for at least one corporate function, leading dedicated Data & Analytics delivery squads.
• Engage with senior business stakeholders, product owners, and architects to understand strategic objectives, define solution approaches, and plan appropriate resourcing for Data Science and AI initiatives.
• Develop compelling business cases for new Data Science and AI projects or enhancements, demonstrating clear value proposition and ROI.
• Lead the evaluation and selection of appropriate Data Science and AI technologies and methodologies, ensuring alignment with long-term strategic needs and enterprise standards.

Technical Leadership & Governance:

• Oversee the end-to-end design, development, and deployment of advanced analytics and AI/ML products, ensuring they are robust, scalable, secure, and well-governed.
• Collaborate with design authorities and platform teams to ensure Data Science and AI solutions adhere to enterprise standards, architectural best practices, and security guidelines.

Qualifications & Experience

Professional Experience:

• 10+ years of overall professional experience in Computer Science, Data Science, Advanced Analytics, or AI/ML.
• 5+ years in a leadership role, managing Data Science or Analytics teams and driving capability maturity.
• Proven track record of successfully delivering impactful Data Science and AI solutions in a complex multi-national environment, from conception to production and value realization.
• Significant experience in enabling data-driven decision making through advanced analytics and machine learning.
• Experience designing and implementing MLOps frameworks and ML Engineering best practices.
• Significant experience in corporate functional domains such as Finance, HR, Procurement, or Legal is a plus.

Technical Expertise:

• AI/ML: Deep expertise in machine learning algorithms (supervised, unsupervised, reinforcement learning), statistical modelling, predictive analytics and Generative AI / Agentic AI for Analytics purposes.
• Programming & Scripting: Proficient in Python (with libraries like scikit-learn, TensorFlow, PyTorch, Pandas, NumPy) and/or R.
• Data Platforms & Cloud: Strong experience with cloud platforms (e.g., AWS, Azure, GCP) and data technologies (e.g., Databricks, Spark, Sagemaker).
• MLOps & ML Engineering: Experience with tools and practices for model versioning, deployment, monitoring, retraining, and pipeline orchestration (e.g. MLflow).
• Data Management: Good understanding of data warehousing, data lakes, ETL/ELT processes, feature stores, and data governance principles.

Methodologies & Practices:

• Strong experience applying agile methodologies (Scrum, Kanban) in project delivery.
• Solid understanding of data governance approaches, data ethics, and responsible AI principles.
• Experience with AI-Ready Architectures and Data Mesh principles.

Key Competencies:

• Strategic Thinking & Problem Solving: Ability to analyze and simplify complex business problems, articulate trade-offs for informed decision-making, and make appropriate recommendations for Data Science and AI solutions.
• Collaboration & Influence: Excellence in collaboration, cross-functional expectation management, and influencing senior stakeholders and technical teams.
• Business Acumen: Strong ability to translate complex business requirements into Data Science problems and deliver actionable insights.
• Stakeholder Engagement: Proven ability to collaborate with diverse teams, build strong relationships, and effectively communicate complex technical concepts to non-technical audiences.
• Innovation: Aptitude for researching emerging Data Science and AI technologies, developing novel solutions, and driving continuous improvement.
• Communication: Excellent written and verbal communication skills in English, with strong presentation and meeting facilitation abilities.
• Drive for Outcomes: Ability to drive initiatives to successful completion, operate hands-on when needed, and focus on delivering measurable business value.

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative field.

Note: 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, gender identity, marital or veteran status, disability, or any other legally protected status. 

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