Senior Data Science Engineer

About Recurly:Recurly, Inc., a SaaS company, provides a versatile subscription management platform to manage the entire subscription lifecycle for market-leading brands worldwide. Subscription businesses such as Sling TV, FabFitFun, Cinemark and Fubo.tv depend on Recurly to harness the power of the subscription model and drive recurring revenue growth. Since its launch in 2009, Recurly has deployed subscription billing for thousands of companies across 55 countries. Our platform empowers billions of credit card transactions and has enabled customers to recover nearly $1.2 billion in revenue in 2023.
Recurly is backed by Accel-KKR, a leading technology-focused private equity firm with over $10 billion in capital commitments. The partnership offers Recurly access to significant capital and resources to make continued investments in technology and platform innovation and expand our go-to-market initiatives.
Recurly is growing! Join us to make subscriptions a competitive advantage for businesses worldwide. Recurly is a leading enterprise subscription billing platform that serves companies of all sizes, including some of the largest Fortune 500 organizations in the world.We are seeking a highly skilled and experienced Senior Data Scientist to lead initiatives in predictive AI and data modeling. The ideal candidate will have a strong foundation in machine learning, advanced statistical methods, and AI-driven predictive modeling techniques. You will be responsible for designing and implementing models that leverage data to predict trends, optimize processes, and drive decision-making.As a Data Scientist at Recurly, you get to collaborate with a team of talented data scientists & analysts, product managers, engineers, and customer success managers to deliver innovative product solutions and research that will continue to solidify Recurly’s market position. Not only will you explore, connect, and produce insights with our data to grow Recurly’s business, but you will also build and deploy production-grade machine learning models that get incorporated into our core product offering. There is a lot more we can do with the volume of data we have, and you’ll play a key role from conception to launch.

Responsibilities

• Data Expert: Understand the breadth and depth of Recurly’s data set to create internal and external facing analyses and insights. Lead and support various ad hoc data analysis projects, as needed 
• Reporting: Build Tableau/Looker dashboards to monitor key business metrics and present data insights in payment and subscription
• Subscription Metrics: Be an expert on subscription business-related metrics and champion those metrics within the company
• Predictive Modeling: Design, develop, and deploy predictive models using state-of-the-art machine learning techniques. These models should identify trends, forecast outcomes, and inform key business decisions.
• AI Development: Lead efforts in the design and implementation of AI solutions to automate processes and improve business outcomes.
• Data Analysis & Interpretation: Extract actionable insights from complex data sets. Develop data-driven solutions and present findings in a clear and concise manner to stakeholders.
• Model Performance Evaluation: Continuously evaluate model performance, ensure model accuracy, and refine models based on performance metrics.
• Collaboration: Work closely with cross-functional teams including product management, engineering, and business units to understand requirements and develop solutions tailored to their needs.
• Data Architecture & Infrastructure: Help design scalable data pipelines and architectures to facilitate efficient access to data, model training, and deployment.
• Mentoring: Provide guidance and mentorship to junior data scientists and analysts, ensuring the team’s growth and fostering an environment of learning.
• Continuous Improvement: Stay up-to-date with the latest trends, tools, and techniques in machine learning, AI, and data science. Recommend improvements in algorithms and technologies for continuous optimization.

Qualifications

• Bachelors in a quantitative field (e.g. Data Science, Computer Science, Statistics, Mathematics etc.). Machine Learning bootcamp or certification is a bonus point for Bachelors
• At least 6 years of relevant working experience, with at least 4 years as a data scientist
• Expert knowledge and demonstrable strong experience with Python, SQL
• Experience developing gold standard training and testing machine learning datasets
• Relevant knowledge in machine learning algorithms such as Deep Learning, LLMs, Random Forest, Gradient Boosting, Logistic Regression, Time Series etc.
• Experience deploying production-grade machine learning modelsDemonstrable experience with statistical analysis (A/B testing,