About the company
Why We Built Lyric: Supply chains are more critical and complex than ever. Every day, large enterprises navigate trillions of possible decisions that could impact the bottom line. Powerful algorithms and AI can address these problems, yet most organizations struggle to leverage supply chain AI at scale - the current SCM technologies are either rigid, limited scope point solutions or require building custom solutions in-house, which demand immense expertise and investment.
That is—until now.
Enter Lyric: Lyric is an enterprise AI platform built specifically for supply chains, offering the best of both worlds:
Out-of-the-box AI solutions for optimizing networks, allocating inventory, scheduling routes, planning fulfillment capacity, promising orders, propagating demand, building predictions, analyzing scenarios, and more, plus
A platform-first approach that empowers both business and technical users with end-to-end composability—leveraging no-code tools, their own code, or even forking our code to build and refine decision intelligence
With Lyric, enterprises no longer have to choose between flexibility and speed—they get both.
The Mission: We’re building a new era in supply chain with the team best equipped to lead it. With over 20 years at the intersection of supply chain and algorithms, we developed a deep conviction that global supply chains needed something like Lyric. Since our inception in December 2021, that conviction has been validated time and time again.
Today, a growing number of Fortune 500 companies—including Smurfit WestRock, Estée Lauder, Coca-Cola, Nike, and more—are innovating on their own terms with Lyric.
We can’t wait to see what our customers—both current and future—are empowered to build with us next. Come build with us.
Position Overview: We are seeking a talented and creative Software Engineer to join our Data Science team at Lyric. As a Software Engineer specializing in Data Science Solutions, you will play a key role in designing, developing, and implementing custom platforms and solutions to support our data science initiatives. You will collaborate closely with data scientists, analysts, and other cross-functional teams to build robust and scalable systems that enable us to derive insights, drive innovation, and make data-driven decisions.
Key Responsibilities:
Platform Development: Design, develop, and deploy custom platforms, tools, and applications to support data science workflows, including data collection, preprocessing, modeling, and visualization.
Infrastructure Architecture: Architect scalable and efficient infrastructure solutions for data storage, processing, and analysis, leveraging cloud services and distributed computing technologies.
Algorithm Implementation: Implement machine learning algorithms, statistical models, and data processing pipelines in production environments, ensuring accuracy, scalability, and reliability.
Integration and Automation: Integrate data science solutions with existing systems and applications, and automate repetitive tasks and processes to streamline workflows and improve productivity.
Performance Optimization: Optimize performance and efficiency of data science platforms and solutions through code optimization, parallelization, and algorithm tuning.
Collaboration and Communication: Collaborate closely with data scientists, analysts, engineers, and stakeholders to understand requirements, define technical solutions, and communicate progress and results effectively.
Documentation and Training: Document technical designs, codebase, and implementation details, and provide training and support to other team members to ensure knowledge sharing and transfer.
Qualifications:
Bachelor's degree in Computer Science, Engineering, Mathematics, or related field.
2+ years of experience in software engineering, with a focus on building data-intensive applications, platforms, or solutions.
Proficiency in programming languages commonly used in data science, such as Python, R, or Scala.
Strong understanding of data structures, algorithms, and software design principles.
Experience with big data technologies (e.g., Hadoop, Spark, Kafka) and cloud platforms (e.g., AWS, GCP, Azure).
Familiarity with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Excellent problem-solving skills, with the ability to analyze complex issues and develop creative solutions.
Effective communication and collaboration skills, with the ability to work in a fast-paced, interdisciplinary environment.
Preferred Qualifications:
Master's degree or PhD in Computer Science, Data Science, or related field.
Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
Knowledge of database systems (e.g., SQL, NoSQL, NewSQL) and data warehousing solutions.
Previous experience working in a data science or analytics role, and familiarity with statistical analysis and experimental design.
Passion for learning and staying updated on the latest advancements in data science, machine learning, and software engineering.
