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.

Job Overview: We are seeking a highly skilled and experienced Senior Data Scientist to join our team. The ideal candidate will have a strong background in time series analysis and a proven track record of applying these skills to solve complex problems in the supply chain domain. This role requires a minimum of 4 years of experience in data science, with a focus on time series forecasting, anomaly detection, and optimization.

Key Responsibilities:

• Develop, implement, and maintain time series models to forecast demand, inventory levels, and other key supply chain metrics.

• Identify and analyze anomalies in time series data to detect potential issues in the supply chain processes.

• Collaborate with cross-functional teams to understand business requirements and translate them into data-driven solutions.

• Design and conduct experiments to evaluate the performance of different models and approaches.

• Communicate findings and insights to both technical and non-technical stakeholders through reports, dashboards, and presentations.

• Mentor junior data scientists and provide guidance on best practices in time series analysis.

• Stay current with the latest advancements in time series methods and technologies, and incorporate them into the team's workflow.

Qualifications:

• Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, or a related field. A Ph.D. is a plus.

• Minimum of 4 years of experience in data science with a focus on time series analysis.

• Proficiency in programming languages such as Python or R.

• Experience with time series forecasting techniques (e.g., ARIMA, SARIMA, Prophet, LSTM).

• Strong understanding of statistical methods and machine learning algorithms.

• Experience with data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn).

• Familiarity with supply chain processes and challenges is highly desirable.

• Excellent problem-solving skills and the ability to think critically and analytically.

• Strong communication skills and the ability to work collaboratively in a team environment.

Preferred Qualifications:

• Experience with big data technologies (e.g., Hadoop, Spark).

• Knowledge of optimization techniques and tools (e.g., linear programming, mixed-integer programming).

• Experience with cloud platforms (e.g., AWS, GCP, Azure).