Job Description

At FourKites we have the opportunity to tackle complex challenges with real-world impacts. Whether it’s medical supplies from Cardinal Health or groceries for Walmart, the FourKites platform helps customers operate global supply chains that are efficient, agile and sustainable.

Join a team of curious problem solvers that celebrates differences, leads with empathy and values inclusivity. 

As a Staff Data Scientist at FourKites, you will collaborate with a team of data scientists and engineers with diverse technical expertise to develop cutting-edge AI-driven solutions. Your primary focus will be leveraging advanced analytics and machine learning to tackle complex challenges in supply chain visibility, driving innovation in predictive modeling, optimization, and real-time decision-making.

In this role, you will have the opportunity to design, implement and deploy scalable AI-powered products, pioneering industry-first solutions that address critical customer pain points in supply chain operations. Utilizing billions of historical data points from FourKites’ Big Data platform, spanning multiple geographies and transportation modes, you will be responsible for building robust, scalable, and production-ready machine learning models that directly impact the global logistics ecosystem.

What you’ll be doing:

• Be a technical thought leader in collaboration with engineering and product leadership, helping to set the strategy and standards for developing Machine Learning and data-driven products at Fourkites. 
• Develop predictive models to optimize supply chain operations and route planning
• Design and implement machine learning solutions for real-time logistics decision-making.
• Analyze large datasets from multiple sources to identify patterns, inefficiencies, and opportunities for improvement.
• Collaborate with cross-functional teams (Engineering, Product, and Business) to integrate data-driven solutions into business processes.
• Lead technical strategy and mentor junior data scientists, setting best practices for data science methodologies and deployment.
• Build and optimize algorithms for ETAs, shipment tracking, vessel scheduling and milestone inferences.
• Develop data pipelines and feature engineering strategies in collaboration with data engineering teams.
• Communicate insights and recommendations to senior leadership, translating complex data findings into actionable business strategies.
• Stay updated with the latest trends in AI/ML, data optimization, and data science applications in logistics.

Who you are:

• 8+ years of experience in data science, machine learning, or applied analytics, preferably in logistics, supply chain, or related industries.
• Strong expertise in machine learning, deep learning, statistical modeling, artificial intelligence and optimization techniques.
• Proficiency in Python, Java, SQL, and distributed computing frameworks (Spark).
• Experience with ML frameworks like TensorFlow, PyTorch, Scikit-learn, XGBoost
• Hands on knowledge working with different databases (Postgres, MongoDB, HBase, Prometheus)
• Solid understanding of operations research and optimization algorithms (e.g., linear programming, reinforcement learning).
• Hands-on experience working with large-scale data pipelines (KStreams, Delta Lake, Redshift, Athena) and cloud platforms (AWS, Azure).
• Ability to translate business problems into structured and scalable data science solutions.
• Strong problem-solving skills and ability to work with ambiguous requirements.
• Excellent communication skills with the ability to influence stakeholders and present data-driven insights.

Preferred Qualification:

• Master's or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical fields.
• The ideal candidate must have expertise in developing products based on Artificial Intelligence (including Deep Learning algorithms), Machine Learning in NLP
• Experience in building, deploying and maintaining Machine Learning models in production environments
• Experience in working with Geospatial data