Software Engineer - Content [Data Science and ML systems] - Lyric

About Lyric

Here at Lyric, we’re building the future of Supply Chain AI, with the team best equipped to build it. Traditional supply chain software, often supplemented with ad hoc analysis, is too rigid for modern AI capabilities. Custom AI solutions are time-consuming, costly to develop in-house, and lack proper supply chain context. These issues make it hard for large supply chain organizations to fully leverage advanced data & decision science.

Our platform, the "Lyric Studio", accelerates your ability to create and consume advanced supply chain models (e.g. stockout prediction & network optimization). A catalog of ready-to-use algorithms and optimization models makes it easy for business users to create sophisticated no-code applications. Lyric Studio also enables technical users to train prediction models & deploy custom algorithms to a private catalog for future reuse. Lyric allows for faster, more advanced supply chain decisions of all shapes and sizes, which is why Fortune 500 enterprises are already saving millions with Lyric today.

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.