Applied Solution Data Engineer

Company Description

Syngenta is a global leader in agriculture; rooted in science and dedicated to bringing plant potential to life. Each of our 28,000 employees in more than 90 countries work together to solve one of humanity’s most pressing challenges: growing more food with fewer resources. A diverse workforce and an inclusive workplace environment are enablers of our ambition to be the most collaborative and trusted team in agriculture. Our employees reflect the diversity of our customers, the markets where we operate and the communities which we serve. No matter what your position, you will have a vital role in safely feeding the world and taking care of our planet. Join us and help shape the future of agriculture.

Job Description

As Applied Machine Learning Engineer (Senior Data Engineer) at Syngenta, you will work within a multidisciplinary global team to discover, define, and design experiences that empower researchers to work more effectively and efficiently by utilizing our data-driven solutions. We are seeking an experienced Senior Data Engineer to join our team and play a crucial role in developing and maintaining data pipelines that support our machine learning models. The ideal candidate will have a strong background in data engineering, machine learning, and experience deploying machine learning models on cloud platforms such as AWS and GCP. As a Generalist, you are adaptable and a flexible problem solver with technical expertise, analytics skills, and product sense to successfully pivot/context-switch amongst projects with a variety of scale and complexity. With your breadth of knowledge, you are often sought after for guidance and mentorship as well. 

In this role, you will work directly with stakeholders and technical partners to design and implement cutting edge data solutions that provide actionable insights to the business. You will be responsible for evolving our long-term roadmap of projects, defining tech stack & operational strategies. You will provide technical mentorship, drive data engineering initiatives and build end-to-end data solutions that are highly available, scalable, stable, secure, and cost-effective. You will work with a wide range of data technologies (e.g. Kinesis, Spark, Redshift, EMR, Hive, and Tableau) and stay abreast of emerging technologies, investigating and implementing where appropriate. You will be responsible for developing the data architecture components that scales for the ever-evolving data needs.

Accountabilities

• Design, build, and maintain data pipelines that support the development and deployment of machine learning models.
• Work closely with data scientists, ML engineers, software engineers, product owners and other team members to understand data needs and ensure data is properly prepared for machine learning models
• Monitor and optimize the performance of deployed models by implementing best practices for logging, monitoring and error handling to triage issues and resolve
• Experience with statistics, analytics, data science, machine learning and their application to solve biological problems
• Experience (5+ years) in Analytics, analyzing data models and product sense
• Experience (5+ years) in ETL design and best practices
• Experience (expert, 7+ years) in writing SQL statements
• Experience with schema design and dimensional data modelling
• Experience managing and communicating data warehouse plans to internal clients
• Knowledge in agile delivery methodologies, including Scrum
• Excellent verbal and written communication skills and the ability to explain complex data to non-experts

Qualifications

Critical experience

• Advanced degree in Computer Science or Engineering or related fields with at least 5 years of relevant experience
• Experience working with either a MapReduce or an MPP system.
• Knowledge and practical application of Python.
• Experience working autonomously in global teams.
• Experience influencing product decisions with data.
• Experience supporting data science function
• Experience working effectively in multi-disciplinary teams
• Experience working with the end-to-end modelling process, including problem formulation and model development, calibration, validation, application and deployment
• Experience in software and product development
• Experience partnering with an IT organization to develop and support software applications
• Experience creating a strong team environment across a large set of separate agile teams and stakeholders
• Experience in change management and digital transformation

Critical technical, professional and personal capabilities

• A “bar-raiser” mentality and the drive to motivate and lead teams to achieve great things
• A passion for innovation and exploration
• Excellent problem analysis and problem solving
• Enthusiastic, communicative, collaborative and results oriented
• Continuously improve the overall data pipeline architecture by researching new technologies and tools that can be used to improve performance and scalability
• Collaborate with cross-functional teams to identify and implement new data sources, and evaluate their suitability for machine learning
• Provide technical leadership and mentorship to junior team members by guiding them on best practices for data engineering and machine learning
• Stay up-to-date with the latest trends and developments in data engineering and machine learning
• Build and own the data architecture for projects, while evaluating design and operational cost-benefit trade-offs within systems
• Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
• Understand and employ optimal ETL patterns, frameworks, query techniques, sourcing from structured and unstructured data sources to solve data problems
• Influence product and cross-functional teams to identify data opportunities to drive impact
• Promote a safe work environment and a strong safety culture

Critical knowledge

• Ability to reduce complexity by focusing on meaningful business outcomes
• Ability to work through ambiguity and uncertainty to deliver results
• Ability to work effectively across disciplines in a global matrix with diverse cultures and skillsets
• Ability to develop and effectively communicate a vision to inspire and motivate teams toward a common key goal
• Ability to influence cross-functional teams and stakeholders
• Strong and proactive leadership, stakeholder management, communication, influencing, facilitation, networking, and conflict management skills

Critical leadership capabilities

• Customer success and profitable market share growth guide our decisions
• We work together to deliver – one team, one plan
• We trust and hold each other accountable
• Our people’s development is a priority, they are our competitive advantage
• We encourage people to say what they think and feel to benefit from diverse perspectives
• We give and welcome prompt and actionable feedback
• We constantly raise performance expectations for ourselves and our teams
• We act on performance issues
• We live by our Code of Conduct
• We are energized by our work and celebrate our achievements

Additional Information

What We Offer:
•    Full benefit package (medical, dental & vision) that starts the same day you do
•    401k plan with company match, profit sharing & retirement savings contribution 
•    Paid vacation, 9 paid holidays, maternity and paternity leave, education assistance, wellness programs, corporate discounts among others.
•    A culture that promotes work/life balance, celebrates diversity and offers numerous family-oriented events throughout the year

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.

Family and Medical Leave Act (FMLA) 
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Equal Employment Opportunity Commission's (EEOC)
(http://webapps.dol.gov/elaws/firststep/poster_direct.htm)

Employee Polygraph Protection Act (EPPA)
(http://www.dol.gov/whd/regs/compliance/posters/eppa.htm)

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