About Lyric

Why we built Lyric: supply chains are as critical & complex as ever → every day, large enterprises are faced with trillions of possibilities for their supply chain → powerful algorithms are required to optimize these supply chain decisions (i.e. AI) → large enterprises are unable to leverage supply chain AI at scale due to (a) the rigidity & limited scope of point solutions (“buy”) and (b) the immense investment required to develop in-house (“build”).......until now (cue dramatic music in the background)......

Enter Lyric: an enterprise AI platform for supply chains - when enterprises evaluate whether to buy or build decision-making engines for their supply chains, we provide the best of both worlds thanks to:

1. Our deep catalog of supply chain AI products that are available out-the-box to optimize networks, allocate inventory, schedule routes, plan fulfillment capacity, promise orders, propagate demand, build predictions, analyze scenarios, and much more (buy)

2. Our platform-first approach enables business & technical users end-to-end composability for supply chain decision science with no-code, their code, or even forking our code (build).

At Lyric we’re building a new era in supply chain, with the team best equipped to build it. We’ve been at the intersection of supply chain & algorithms for 20+ years, which led us to a deep conviction that global supply chains needed something like Lyric. We’re proud to say that since our inception in Dec. 2021, this conviction has now been validated many times over. This is thanks to our double-digit number of Fortune 500 Customers (Smurfit WestRock, Estee Lauder, Coca-Cola, Nike, and more) who are innovating on their own terms every day on Lyric. After an incredible 2024, we expect 2025 to be even more of a rocket ship, and we can’t wait to see what our current & future customers are empowered to build with us next.

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:

1. Platform Development: Design, develop, and deploy custom platforms, tools, and applications to support data science workflows, including data collection, preprocessing, modeling, and visualization.

2. Infrastructure Architecture: Architect scalable and efficient infrastructure solutions for data storage, processing, and analysis, leveraging cloud services and distributed computing technologies.

3. Algorithm Implementation: Implement machine learning algorithms, statistical models, and data processing pipelines in production environments, ensuring accuracy, scalability, and reliability.

4. Integration and Automation: Integrate data science solutions with existing systems and applications, and automate repetitive tasks and processes to streamline workflows and improve productivity.

5. Performance Optimization: Optimize performance and efficiency of data science platforms and solutions through code optimization, parallelization, and algorithm tuning.

6. Collaboration and Communication: Collaborate closely with data scientists, analysts, engineers, and stakeholders to understand requirements, define technical solutions, and communicate progress and results effectively.

7. 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.