Machine Learning Engineer

Key Skills

Java Programming, Design, Python Programming, SQL, AWS, Customer Support, Machine learning techniques, Data science techniques, A/B Testing, Data Visualization, NumPy, Pandas, SciPy, Docker, Kubernetes-K8s, Scikit-learn, Kubeflow, MLflow, matplotlib, Data Engineering, Data pipelines, Mathematics, Terabyte scale datasets, Data Analysis

Job Description

Machine Learning Engineer will work in multidisciplinary teams to develop scalable ML systems for our predictive pricing models. In this role, you will conceptualize and develop robust ML pipelines, deploy to production, and maintain them for our multiple businesses.

Strategic Pricing Center of Excellence is a central team that works with Cargill’s various Food Ingredients and Bio-industrial (FIBI) businesses. Our mandate is to elevate Strategic Pricing capabilities and practices for FIBI businesses. As part of this, we aim to develop predictive pricing models leveraging Cargill’s vast data supply. To accomplish this, we are looking to hire ML Engineer who will build scalable ML systems for our predictive models.

As a ML Engineer within the Strategic Pricing Center of Excellence, you will work with pricing and other commercial teams, IT, and data scientists to build ML models that enable value-based pricing across Cargill. You will work on exciting projects in multidisciplinary, cross-cultural and collaborative teams.

Key Accountabilities

• Work with multidisciplinary teams of data engineers, software engineers, data scientists and business subject matter experts to deliver complex projects on time and within budget
• Take ownership for key components of data science workflow including model deployment, and monitoring
• Develop scalable ML systems and support inference, monitoring, retraining and data pipeline development
• Transfer machine learning solutions from R&D to Testing to Production
• Build readable, maintainable, and testable code, using standard coding methodologies, and propose, design, and develop the ML systems at scale
• Monitor platform health, performance, and business impact by creating dashboards
• Maintain multiple models in production and build resilient model monitoring system
• Continuously evolve AI/ML craft by keeping up to date with the latest developments in AI/ ML
• Support Science Capability Lead in realizing the product vision, technical feasibility studies and product management
• Other duties as assigned

Qualifications

Minimum Qualifications

• Bachelor's / Master’s degree in data science, computer science, math, engineering or related field from Tier I college
• 3+ years of work experience solving business problems with the application of Machine Learning
• Proficiency in python programming language is a must
• Experience running Machine Learning systems in production is a must
• Knowledge in python packages for data analysis (scikit-learn, scipy, numpy, pandas, matplotlib)
• Understanding of MLOPS life cycle experience with MLOPS workflows traceability, versioning of datasets
• You have a ‘can do’ attitude and you act proactively and not reactively

Preferred Qualifications

• Master's degree or PhD in data science, computer science, math, engineering, or related field
• Hands-on experience with Machine learning life cycle operations e.g. MLflow/Kubeflow etc.
• Experience with data-driven product development: Analytics, A/B testing, etc.
• Experience with one or more Container-ecosystem (Docker, Kubernetes)
• Strong data visualization skills
• Basic SQL skills
• Experience working in a cloud environment (AWS is a plus)
• Follows good software engineering practices and has an interest building reliable and robust software
• Experience in pricing modelling